The slide has details on below points:
1. Introduction to Machine Learning
2. What are the challenges in acceptance of Machine Learning in Banks
3. How to overcome the challenges in adoption of Machine Learning in Banks
4. How to find new use cases of Machine Learning
5. Few current interesting use cases of Machine Learning
Please contact me (shekup@gmail.com) or connect with me on LinkedIn (https://www.linkedin.com/in/shekup/) for more explanation on ML and how it may help your business.
The slides are inspired by:
Survey & interviews done by me with Bankers & Technology Professionals
Presentation from Google NEXT 2017
Presentation by DATUM on Youtube
Royal Society Machine Learning
Big Data & Social Analytics Course from MIT & GetSmarter
presentation on MARC21 Standard Bibliography for LibMSMuhammad Zeeshan
I have developed a module named MARC21 standard bibliography (fixed and customized) for LibMS(An online library management system developing by Aligarh Muslim University, Aligarh and owned by MHRD, Govt. of INDIA).
The slide contains the description about MARC21 standard cataloging and the module which I have developed.
When we talk about LIS education, we’re talking about providing education for a professional career in libraries, with all the traits the word ‘profession’ implies: professionalism, prolonged training, and formal education. This type of education wasn’t always the case however; it developed as the librarian profession did. In fact, the creation of library schools had a direct impact on making librarianship a professional career in the first place.
A weblog or blog is a web page that consists brief paragraphs of opinions, commentaries, description of events or other materials such as graphics or videos.
It is usually maintained by an individual.
Entries are commonly displayed in reverse chronological order, which means the most recently added piece of information appears first.
Library blogs can be used for different purposes.
Blogs as an academic library services
New arrivals
CAS
Acts as a portal to web resources
News alerts
‘Ask the librarian’ and ‘suggest a book’ pages
Photos and videos of seminars, conferences, lectures etc.
Library blogs can be used for different purposes.
Blogs as an academic library services
New arrivals
CAS
Acts as a portal to web resources
News alerts
‘Ask the librarian’ and ‘suggest a book’ pages
Photos and videos of seminars, conferences, lectures etc.
presentation on MARC21 Standard Bibliography for LibMSMuhammad Zeeshan
I have developed a module named MARC21 standard bibliography (fixed and customized) for LibMS(An online library management system developing by Aligarh Muslim University, Aligarh and owned by MHRD, Govt. of INDIA).
The slide contains the description about MARC21 standard cataloging and the module which I have developed.
When we talk about LIS education, we’re talking about providing education for a professional career in libraries, with all the traits the word ‘profession’ implies: professionalism, prolonged training, and formal education. This type of education wasn’t always the case however; it developed as the librarian profession did. In fact, the creation of library schools had a direct impact on making librarianship a professional career in the first place.
A weblog or blog is a web page that consists brief paragraphs of opinions, commentaries, description of events or other materials such as graphics or videos.
It is usually maintained by an individual.
Entries are commonly displayed in reverse chronological order, which means the most recently added piece of information appears first.
Library blogs can be used for different purposes.
Blogs as an academic library services
New arrivals
CAS
Acts as a portal to web resources
News alerts
‘Ask the librarian’ and ‘suggest a book’ pages
Photos and videos of seminars, conferences, lectures etc.
Library blogs can be used for different purposes.
Blogs as an academic library services
New arrivals
CAS
Acts as a portal to web resources
News alerts
‘Ask the librarian’ and ‘suggest a book’ pages
Photos and videos of seminars, conferences, lectures etc.
Collection evaluation techniques for academic libraries ALISS
Sally Halper, Lead Content Specialist - Business & Management, British Library. An excellent introduction to some really good practical qualitative and quantitative tools including White's brief tests. A bibliography of further readings is also provided.
Introduction to MARC
History (MARC to MARC 21)
Why MARC 21/Need of MARC 21
Characteristics
Design principle for MARC 21
MARC 21 Documentation
MARC 21Record System
MARC 21 Communication formats
MARC 21 Format for Bibliographic Data
Component of bibliographic record
Communication Standard
Mapping of MARC 21
MARC 21 Translation
Maintenance Agency
MARC 21 Regulation
Advantage of MARC 21
Problems with MARC 21
Future of MARC 21
Presenters: Rebecca Hunnicutt
Presented at the Georgia Libraries Conference in Macon, GA on 10/11/2019.
Creating a call number for an item is a necessary step in the cataloging process in any technical services department. However, it can be a surprisingly complex task. Creating a call number requires the use of standardized rules as well as a
basic knowledge of call number structure.
Creating a New Bibliographic Record in Koha with MARC 21 FieldsPatit Paban Santra
This PPT actually shows the step by step process to create a new Bibliographic record on Koha ( Library Automation Software) with MARC 21 fields. Its deals with Library and Information Science domain.
This is an archive on a webinar delivered on January 12, 2012. Description: If you’re really new to cataloging, this session is for you. In this 90-minute online session, facilitated by NEKLS technology librarian Heather Braum, you will:
learn the basic principles behind cataloging,
discover why librarians catalog,
learn to read a basic MARC record,
see what a good MARC record looks like,
learn basic cataloging terminology,
and practice describing different materials.
Special thanks to Robin Fay for allowing me to use a couple of the ideas shared in this webinar and presentation. See her outstanding slides: http://www.slideshare.net/robinfay/cataloging-basics-presentation.
BIG DATA AND MACHINE LEARNING
Big Data is a collection of data that is huge in volume, yet growing exponentially with time. It is a data with so large size and complexity that none of traditional data management tools can store it or process it efficiently. Big data is also a data but with huge size.
Exploring the Differences Data Mining vs. Machine LearningAndrew Leo
In today's data-driven world, understanding the nuances between data mining and machine learning is crucial. While often used interchangeably, they serve distinct purposes in the realm of analytics and AI. Check out our latest post to delve into the disparities between these two technologies and how they shape decision-making processes.
Ready to harness the power of data? Contact us today for expert insights and solutions!
Collection evaluation techniques for academic libraries ALISS
Sally Halper, Lead Content Specialist - Business & Management, British Library. An excellent introduction to some really good practical qualitative and quantitative tools including White's brief tests. A bibliography of further readings is also provided.
Introduction to MARC
History (MARC to MARC 21)
Why MARC 21/Need of MARC 21
Characteristics
Design principle for MARC 21
MARC 21 Documentation
MARC 21Record System
MARC 21 Communication formats
MARC 21 Format for Bibliographic Data
Component of bibliographic record
Communication Standard
Mapping of MARC 21
MARC 21 Translation
Maintenance Agency
MARC 21 Regulation
Advantage of MARC 21
Problems with MARC 21
Future of MARC 21
Presenters: Rebecca Hunnicutt
Presented at the Georgia Libraries Conference in Macon, GA on 10/11/2019.
Creating a call number for an item is a necessary step in the cataloging process in any technical services department. However, it can be a surprisingly complex task. Creating a call number requires the use of standardized rules as well as a
basic knowledge of call number structure.
Creating a New Bibliographic Record in Koha with MARC 21 FieldsPatit Paban Santra
This PPT actually shows the step by step process to create a new Bibliographic record on Koha ( Library Automation Software) with MARC 21 fields. Its deals with Library and Information Science domain.
This is an archive on a webinar delivered on January 12, 2012. Description: If you’re really new to cataloging, this session is for you. In this 90-minute online session, facilitated by NEKLS technology librarian Heather Braum, you will:
learn the basic principles behind cataloging,
discover why librarians catalog,
learn to read a basic MARC record,
see what a good MARC record looks like,
learn basic cataloging terminology,
and practice describing different materials.
Special thanks to Robin Fay for allowing me to use a couple of the ideas shared in this webinar and presentation. See her outstanding slides: http://www.slideshare.net/robinfay/cataloging-basics-presentation.
BIG DATA AND MACHINE LEARNING
Big Data is a collection of data that is huge in volume, yet growing exponentially with time. It is a data with so large size and complexity that none of traditional data management tools can store it or process it efficiently. Big data is also a data but with huge size.
Exploring the Differences Data Mining vs. Machine LearningAndrew Leo
In today's data-driven world, understanding the nuances between data mining and machine learning is crucial. While often used interchangeably, they serve distinct purposes in the realm of analytics and AI. Check out our latest post to delve into the disparities between these two technologies and how they shape decision-making processes.
Ready to harness the power of data? Contact us today for expert insights and solutions!
In this ppt, you can learn about the Top 10 Trends to Watch for In Data Science. You must see this ppt till the end, written by experts of data science institute in GTB Nagar Delhi.
Machine learning(ML) is the scientific study of algorithms and statistical models that computer systems used to progressively improve their performance on a specific task. Machine learning algorithms build a mathematical model of sample data, known as “Training Data", in order to make predictions or decisions without being explicitly programmed to perform the task. Machine learning algorithms are used in the applications of email filtering, detection of network intruders and computer vision, where it is infeasible to develop an algorithm of specific instructions for performing the task. Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a field of study within machine learning and focuses on exploratory data analysis through unsupervised learning. In its application across business problems, Machine learning is the study of computer systems that learn from data and experience. It is applied in an incredibly wide variety of application areas, from medicine to advertising, from military to pedestrian. Any area in which you need to make sense of data is a potential customer of machine learning.
Advanced Analytics and Data Science ExpertiseSoftServe
An overview of SoftServe's Data Science service line.
- Data Science Group
- Data Science Offerings for Business
- Machine Learning Overview
- AI & Deep Learning Case Studies
- Big Data & Analytics Case Studies
Visit our website to learn more: http://www.softserveinc.com/en-us/
Data and Analytics Career Paths, Presented at IEEE LYC'19.
About Speaker:
Ahmed Amr is a Data/Analytics Engineer at Rubikal, where he leads, develops, and creates daily data/analytics operations, which includes data ingestion , data streaming, data warehousing, and analytical dashboards. Ahmed is graduated from Computer Engineering Department, Alexandria University; and he is currently pursuing his MSc degree in Computer Science, AAST. Professionally, Ahmed worked with Egyptian/US startups such as (Badr, Incorta, WhoKnows) to develop their data/analytics projects. Academically, Ahmed worked as a Teaching Assistant in CS department, AAST. Ahmed helps software companies to develop robust data engineering infrastructure, and powerful analytical insights.
References:
1) https://www.datacamp.com/community/tutorials/data-science-industry-infographic
2) Analytics: The real-world use of big data, IBM, Executive Report
Gary Hope - Machine Learning: It's Not as Hard as you ThinkSaratoga
Gary Hope is currently the Data Platform Technical Specialist within Microsoft South Africa having previously worked for several large organisations including American Express and Siemens Business Solutions.
Slides from talks presented at Mammoth BI in Cape Town on 17 November 2014.
Visit www.mammothbi.co.za for details on the event. Follow @MammothBI on twitter.
Ethical AI: Establish an AI/ML Governance framework addressing Reproducibility, Explainability, Bias & Accountability for Enterprise AI use-cases.
Presentation on “Open Source Enterprise AI/ML Governance” at Linux Foundation’s Open Compliance Summit, Dec 2020 (https://events.linuxfoundation.org/open-compliance-summit/)
Full article: https://towardsdatascience.com/ethical-ai-its-implications-for-enterprise-ai-use-cases-and-governance-81602078f5db
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
4. Machine that can think and work like
humans, but free from human errors and
biases, can harness the big data and high
computational power, runs on highly
sophisticated algorithms is the next frontier
in technological arms race
5. Machine Learning: Approach
• “Machine learning is a method of data analysis that automates analytical model
building. Using algorithms that iteratively learn from data, machine learning
allows computers to find hidden insights without being explicitly programmed
where to look.” – SAS
• Let machine learn how best to achieve the desired output. Processes data in a
way that machine can detect and learn from patterns, predict future activity,
and make decisions. Data is fuel: More the data, better the learning.
6.
7.
8. 18th century also saw one of the most influential data
visualization (a map) of all time, produced by John
Snow. John Snow disapproved the then dominant
miasma theory that stated that diseases such as
cholera were caused by noxious bad air.
In 1854, London, Cholera strike killed many, then, John
Snow collected data of all deaths, analysed it, and
plotted every death on map. Later, John Snow used
dot map to illustrate the cluster of cholera cases
around the pump. The map shows the location of the
13 public wells in the area and 578 cholera deaths
mapped by home address, around Soho district. It was
evident from map that cases were clustered around
the pump in Broad (Broadwick) street. Map was part
of the detailed statistical analysis done by Snow. Snow
interpreted and theorized that cholera was spread
through the contaminated water.
Quality Data and Powerful visualisation results in right
interpretation
And finally intervention: A new sewer system.
9. How to measure Data Quality
• 5 R’s of Data Quality
• Relevancy: Is the data collected is relevant to the problem we are
solving?
• Recency: How recent was the data generated?
• Range: How narrow or wide is the scope of data?
• Robustness: What is signal to noise ratio?
• Reliability: How accurate is the data that we are working with?
• Ask Questions:
• What is my target data quality?
• What if only 50% of data is available and How sensitive is analysis to
missing data?
• What signal will be used to understand the quality of data?
• Ephemeral vs Durable
• Noise vs Bias
14. Machine Learning
• Machine Learning is a branch of artificial intelligence that allows computer
systems to learn directly from examples, data, and experience.
• Through enabling computers to perform specific tasks intelligently, ML systems
can carry out complex processes by learning from data, rather than following pre-
programmed rules.
• Machine Learning relies on data and does different type of analysis:
• Descriptive (data mining) - Analyse the data for creating approach for future such as
detecting anomalies in current data based on past data;
• Predictive (forecasting) – Turn data into valuable information for prediction such as when
problem will occur;
• Prescriptive (optimization) – synthesize big data and suggest decisions.
• One of the most important paper on ML is written by Pedro Domingos in 2012 in
which he summarized the key lessons of Machine Learning
15. Machine Learning: Predictive Model Building
• Data collection (predictors and response)
• Exploratory data analysis (EDA)
• Data Preprocessing
• Assigning numerical values to categorical data
• Handling missing values
• Normalizing the features (so that features on small scales do not dominate when fitting a model to the data).
• Algorithm selection
• Model selection
Parametrized mapping (function or process) between the data domain and the response set, which learn the
characteristics of a system from the input (labelled) data. Modelling may have several algorithms to derive a
model; however, the term algorithm here refers to a learning algorithm. The learning algorithm is used to train,
validate, and test the model using a given data set to find an optimal value for the parameters, validate it, and
evaluate its performance.
• Model evaluation
• AUC
16.
17. The Learners
Thousands of ML algorithms exist and hundreds get added with time (Domingos, 2012). A learner
can be mix of learning algorithms. Algorithms can be grouped or understood of classified in two
ways: One way is the learning style and other is similarity in function. There are different ways an
algorithm can model a problem based on its interaction with the input data and it is popular to first
consider the learning styles that an algorithm can adopt (Brownlee, 2013):
• Supervised learning, system is trained with data that is labelled, for example, machine
trained with normal or suspicious labelled previous credit card transactions.
• Unsupervised learning, the objective is to identify or infer a function to describe a hidden
structure or similarity of patterns in unlabelled data, such as finding clusters in graph or
which products customer can buy next.
• Semi-supervised learning, Input data is mixture of labelled and unlabelled data.
• Reinforcement learning, focuses learning from experience in which machine learns the
consequences of its decisions, such as which moves lead to win in game. In IT operations,
reinforcement learning enables a self-healing system that learns what actions need to do to
recover from an incident, increase data flows, and optimize operations (Kaluza, 2015)
18. Machine Learning – The time is now
• The recent success of AI has got lot of media attention because of the immersive experience
that users are seeing and experiencing and many believe soon existing software’s, trading
algorithms, fund managers, and banking systems may be replaced by AI.
• Machine could be customer service executive, banker, and next Warren Buffet.
• ML – a narrow Artificial Intelligence – is booming and media attention is all time high right now.
• “The future is already here – It’s just not evenly distributed” – William Gibson
• “Machine intelligence is the last invention that humanity will ever need to make.” (Bostrom,
2015)
• Either integrate slowly or adopt rapidly, Banks have to do it, Banks definitely need a roadmap
21. Disruptive
Technology
• The difference in the technological frames of
managers and technologists, Complacency
with existing systems, and Organisational
Agility to adopt new technology?
• Established firms are more inclined to invest
in sustaining technologies (Christensen, C.
M., 1997)
22. Technology
Competence
• Managers understanding of ML model and
assuming ownership of it?
• Decision makers have different level of
understanding of data: many find data
mysterious, confusing, and distant
• Interpretability of ML models?
23. Trust
• Analytical processes hidden between Layers – Will
they be accepted by Regulators?
• What’s risk?
• Will data sharing lead to privacy breach?
• How model is making decision and what is
accuracy?
• Auditing?
• Costs vs Benefits?
26. Data & Information Silos
• A data silo is a repository of data that remains under the control of one
department and is isolated from the rest of the organization.
• Distributed data is the biggest challenge in realising the potential of data.
• Traditionally software applications are written at one point in time and
optimised for their main business function creating data silos.
• Political groups within organisation are suspicious of others wanting their
data
• Banks have lived through multiple leaders, mergers & acquisitions, long
existing divisions, legacy IT systems, and philosophies, resulting in
distributed and incompatible systems.
• Vendor lock-in, software vendors are good in making customers transition
to another product tough by making hard the data extraction & migration
hard from their product.
28. Promote favourable patterns
• Articulate interactions between managers and technologists for common
interpretation of technology
• Organizational Agility is collective of members agility and creativeness
• “Design strategies that enhance the ability of humans to understand AI systems
and decisions (such as explicitly explaining those decisions), and to participate in
their use, may help build trust and prevent drastic failures” – Stanford University
research report
• Open up the discussion across bank, create boundaries, simulate attractors,
encourage dissent and diversity, manage starting conditions and monitor for
emergence (of patterns), and brainswarming
• Duplicate data, log data, and present it when needed
• Embrace opportunity with right mindset and tools
• Robust IT architecture to support changes
31. Visualize the IT
Rabobank has invested in a 3D model of its IT
landscape mapping departments and IT systems: two
models, one representing current and one
representing the future (target), after 3 months of
gathering information. Rabobank has now also built
the 3D model in virtual reality, in order to add an
extra layer to the experience. There are also plans to
build a virtual model using hologram techniques.
Current situation in image, departments on top,
systems below, connections in between.
32. Data Governance
• The upgraded enterprise data governance should focus on unifying the
people, processes, data, and technologies. New age data governance
aimed at ending the bureaucracy within Banks will organize the free flow of
information but at the same time maintain data quality and deal with
security and regulatory challenges.
• For years IT professionals convinced business leaders that it is technical
thing and they should control the data governance decisions. But,
pragmatic and ease to use comes to fore as business leaders from different
verticals realize their teams can make numerous decisions using powerful
tools but still controlled within rules, laws, regulations, and ethics.
• Data Governance 2.0 is about an agile approach to data governance
focussed on just enough controls for managing risks, enabling more
insightful use of data.
33. Approaches
• Data Governance is an iterative cycle with need focussed on master
data management and business case
• The elements of strategy are both defence (SSOT) and offense
(MVOT) (source: HBR)
• Options:
• Cold Storage
• Data Lake
• Cloud Data warehouse
• Logical Data Warehouse
• Specialised tool for use case basis
34. Vendors
• COLLIBRA is recognised as leader in Data Governance platforms by Gartner and Forrester
• INFORMATICA and IBM have build Data Governance platforms for long time
• TRIFACTA try to solve the data preparation problem with a belief that people who know
data should be able to wrangle the data themselves. Trifacta sits between the data
storage and ML models and provides many facilities such as Connectivity, Metadata
management, Processing, Intelligence, Wrangling, Transformation, and Publishing.
Trifacta is a data wrangling platform that helps business analysts to intuitively wrangle
data themselves without relying on IT engineers (trifacta, 2017)
• PAXATA is reliable platform for solving the data preparation as the platform beings
together multi-structured data from diverse sources (paxata, 2017)
• Many Banks while adopting Machine Learning will start small – use case by use case - but
without complete analysis of the data governance they may either face scalability
problems in later stages and they will run ML models in silos. Banks can opt for building
a unified architecture for data governance that is available to all and it is scalable.
DATABRICKS is a unified analytics platform. (databricks, 2017)
35. Machine in Context
• Machines are strategic assets.
• The ML programmers have to guide the exploration of data in ways
that support the goals of organisation; they need to put hat of
strategic analysis not just data analysis
• ML programmers have to be aware of other parts of company, know
problems of company in depth, and be more valuable
• ML programmers should know the broad context in which to put
machine learning.
• As a manager, ask yourself a question, “How would you describe the
role of machine in your organisation?”
36. Where to use Machine Learning
finding new use cases of Machine Learning
37. Finding use case for Machine Learning – Find business problem
The business problem can of simple or complicated and if Machine Learning
can help solve the problem can be decided by asking questions:
• Q: Think of one or more business problems of your Bank that are un-solved or can be
solved better? Problems that are:
• Complicated – Not straight-forward – Can’t be solved by standard software or automation –
OR not clear pre-defined sequence of steps
• A learning from data is required such as prediction – more than casual inference – need to
know certain aspects of data related to each other
• Problem is sufficiently self-contained, relatively insulated from outside influences
Business problem should have all three characteristics to be a potential problem to be solved by
Machine Learning.
One knowing that problem fits in ML domain, further two important questions to
answer are:
• Q: Whether right data exists for the problem? Where does it comes from? Is data
feed for machine sufficient to solve the problem?
• Q: Which ML method makes more sense to the problem?
38.
39. Does business problem matched ML canonical problem
Canonical Problem Description Question
Classification When to choose in which category data belongs or
predict the category of data. Categories can be
only two (Yes or No, Fraud or Not Fraud) in case of
binary classification or more in case of multi-class
classification.
To which category this data point
belongs?
Regression When to predict a value such as stock price. Given the input from dataset, what is
likely value of particular problem?
Clustering When the data is unlabelled and complex,
organising it to look simpler, then unsupervised
learning is called clustering such as finding clusters
in network
Which data points are similar to form
cluster?
Dimensionality
reduction
When dataset has huge number of features or
variables or columns then select most effective
features so that implemented ML model is simple,
faster, and reliable.
What are the most significant
features of this data and how can
these be summarised?
Semi-supervised
learning
When dataset is combination of labelled and
unlabelled.
Anomaly Detection:
When to identify data points that are simply
unusual such as detecting the breach or fraud. The
training dataset will be small and possible
variations are numerous. Anomaly Detection
learns what is normal activity (on small training
data and in real time i.e. online) and identifies
anything significantly different.
How can detection be developed
with small training data set?
Reinforcement learning The learning algorithm takes action for each data
point and receives reward if decision was good.
Algorithm modifies its strategy to gain maximum
rewards, for example in Gaming and Robotics
What actions will achieve most
effectively desired output?
The next table, taken from Royal
Society report, classifies the problems
that ML solves; therefore, ML
classification is done based on the
problems.
Canonical problems are the
fundamental problems that ML seeks
to solve.
If identified Business Problem is one of
the Canonical Problem, then business
problem can be solved by Machine
Learning.
Multiple algorithms exist for each
Canonical problem and Banks will have
to experiment with them regarding
which one suits best. Experienced ML
programmers and data scientist can
tell more easily about right ML
algorithm or combination of ML
algorithms.
40. ML Canonical Problem and examples
Canonical Problem Applications
Classification Face recognition: Identify people by their face for identity verification and surveillance
Image recognition: Picture is of car or not car to be used during insurance claims.
Fraud detection: Based on customer features predict if customer can repay loan
Classification: Customer sentiment is positive or negative for new product
Regression Financial forecasting: What stock price will be in future
Click rate prediction: Probability of customer clicking ad
Pricing: House price prediction
Future performance: Include financial data, news, sentiments, social media, and press releases to predict
future of companies.
Clustering Document modelling: Find patterns and structures in documents
Network analysis: Find clusters in network
E-commerce: Customers with similar interests
Dimensionality
reduction
Data mining: Find best performing features from high dimension data
E-commerce: Which features summarize our targeted customer
Semi-supervised
learning
Anomaly Detection: Cybersecurity, Financial Terrorism
Reinforcement
learning
Trading
Gaming: AlphaGo
42. Machine Learning Use Cases
• ZOPA
• HashtingsDIRECT – Insurance Company
• Fraud prevention in PayPal – Payments
• Numerai - Hedge Fund relies on an anonymous army of coders
• Neurensic - Policing the Stock Market
• Google Cloud ML Engine
• Machine Learning in Insurance
• Data Governance using Machine Learning
• Reducing the credit Gap
43. ZOPA – peer-to-peer lending platform
ZOPA uses ML in everything it does: Credit Risk, Fraud, ID verification, Document tampering, Pricing, and Customer
Segmentation.
For Credit Risk, Journey of ML: Gathering the Dataset, Defining the target, Feature optimisation, ML model building,
and Model stacking.
Dataset for Credit Risk comes from Credit agencies – Mortgages, credit cards, accounts, payments, and so on – more
than 3000 features for 100s thousands of borrowers. Firstly, the dataset is cleaned – handling null and rare values
and converting category values into numbers – and then normalization. 3000 features are reduced to tens using
feature selection (dimensionality reduction) for easier, faster, and more reliable implementation. Features are
selected based on importance. Based on features selected, ML model predicts (binary classification) is customer can
repay the loan or not. For prediction, ZOPA, experimented many ML algorithms found that combination of Neural
networks and GBT worked best.
44. HashtingsDIRECT – Insurance Company
• Business Problem is to predict:
• How many accidents customer is likely to have?
• How much accidents are going to cost?
• The datasets are imbalanced: Many customers don’t have
accidents and many accidents are not expensive.
• Data comes from several sources (3rd parties and customers),
at different quality, and different stages (claims, location,
vehicle, and government)
• Challenges in insurance industry are: managing quotes, legacy
IT, long feedback loops (customer can claim after years), and
conservative industry (not allowed to use few fairly predictive
features such as Gender).
• A combination of supervised and unsupervised learning is
done.
• ML can’t run on current legacy IT, so, IT will be upgraded
45. Fraud prevention in PayPal – Payments
• PayPal uses Oracle for online transaction but for offline processing PayPal uses Hadoop.
• PayPal has 15000 features engineered over years in repository to build ML models over
it.
• ML models are applied at transaction level (when users make payment). More
sophisticated models are applied after transaction completes.
• Fraud prevention is a supervised learning problem and PayPal uses Neural Networks (as
algorithm).
• Current ML model had AUC of 0.96, but PayPal decided to improve it by improve
labelling.
• Labelling in PayPal was human driven and PayPal tested if it can be improved using ML.
• PayPal used ML Algorithm - Active Learning (see Notes) - over heuristic user labelling to
improve the labelling quality that in turn improves the data that is feed into Supervised
ML models.
• Finally improving AUC from 0.96 to 0.979. Previously it used to take a week for labelling
but with use of Active Learning labelling effort has come down to 30 minutes.
(See Notes)
47. Numerai - Hedge Fund relies on an anonymous army of coders
• Entirely anonymous (if they want) 7500 developers receive trading data to make
forecast using ML. If predictions are useful, they get paid in cryptocurrency.
• Since industry has been under fire for being overpriced and underperforming, ML
can munch extra data that humans and regular algorithms couldn’t make sense.
• Numer.ai wants to transition the negative competition between massive hedge
funds into highly valuable collaborative space to create the first hedge fund with
network effect.
• Numerai brings crowd intelligence – harness people who don’t want to take it as
a day job but they are good in data handling, AI, and statistics, and they don’t
have resources to start a hedge fund - capturing the intelligence of crowd in
which any one can participate and bring it to stock market.
• It’s a math problem and one doesn’t need to know any finance but can share the
his or her data science skills with Numerai and Numerai will pay by making
money in stock market. Another benefit of ML that Numerai is utilizing is many
ML models can run on encrypted data.
48. Numerai - Hedge Fund relies on
an anonymous army of coders
• Numerai had a notion of meta model - one big benefit of ML comes from
combining different ML models such as one model perfectly predicts only
utility stocks and another model perfectly predicts bank stocks and meta
model can be created combining both models.
• Every data scientist on Numerai is solving the same problem using the
same underlying features. But every data scientist approaches the problem in
their own unique way. With many different solutions to the same problem,
Numerai is able to combine each model into a meta model just like Random
Forests combines decision trees into a forest.
• Logloss error of the best Numerai data scientists — Numerai’s meta
model has lower error than any individual model.
49. Neurensic - Policing the Stock Market
• Economy worldwide is priced on capital market and Most of the trading is algorithmic
but surveillance has been manual for a very long time.
• Billions of dollars are spent by financial institutions on trade surveillance alone. Trade
surveillance is a trillion-dollar problem in billion-dollar industry that is based on 20 years
old technology.
• Neurensic look financial data for illegal activities by applying ML to the market. Data
comes from all kind of exchanges (Chicago, NASDAQ, London, and so on), audit logs,
clearing houses, and bunch of systems. The machine has to be intelligent because data is
big (terabytes scale - trillion rows daily), signal is very small, and algorithms have to be
updated (change) rapidly because of the changes in ways of illegal things.
• Since Neurensic is not a law enforcement organisation, also it can't declare someone's
intent, but it can monitor ones behaviour and with help of past data (activities that have
been investigated or prosecuted before) and domain experts, can give it a risk score, and
bring it in front of compliance officer. Finding the illegal or questionable activity is the
first step and next is to explain why? especially when ML are notorious for being opaque.
50. Neurensic - Policing
the Stock Market
• Pull the data (audit logs and market data), filter it
down to last step (from billions to 100s), put compelling
visualizations, and explain step by step. Visualisations of
raw data is a key because that is real evidence (not ML
results) - ML runs on heavy processed data but in order to
present findings one needs to go back raw data and
possibly past transactions line by line.
• Visualizations can be graphs, movies - show trades in
real time in slow - tick by tick, and new visuals for new
patterns. Browser and Mobile based visualizations that
can be displayed to CxO's and lawyers. All data science in
Neurensic is in Python running on H2O.ai (premier open
source ML tool with cutting edge ML algorithms) and
within minutes DS algorithm goes from research to
production.
51. Neurensic - Policing
the Stock Market
• Audit logs (in CSV format) are pulled into H2O,
which is very good in compressing; the data is then
made ready, using ETL – handle missing values,
normalize, uniform mapping for tokens and products
from different sources, and clean-up for ML; Sorted
and each CPU gets equal proportion of data to
process in parallel; Clustered – depending on the ML
model requirements data is segregated and grouped
such as transactions close in time or transactions for
same instrument; define features and run ML models
on clusters in sequence on each CPU; and risks file are
generated based on type of risk. Risk file has lot of
details and a pointer to raw data.
52. Google Cloud ML Engine
• Organisations moving to cloud and using Google Cloud ML engine will have most of the
work only related to cleansing of data (estimated 90%).
• Currently Google is using ML in almost all of its products
• Google is providing the ML as a service (Application Programming Interface, API)
• Benefit of using Google API’s is that they are already tested on huge data and are in
production for few years so organisations using those API’s can avoid training the ML
• API’s can be used to solve many business problems, such as API’s related to image and
video can be used in Insurance industry (see Notes)
• API’s related to speech, natural language processing (NLP), language translation, and so
on can be used for creating Video assistant, Face Banking, chat bots, or enhance existing
applications
• Many Banking use cases, running on Google Cloud are already using API’s such as Risk
analytics, Regulations, Customer Segmentation, Cross-selling and up-selling, Credit risk,
and fraud detection
(See Notes)
53. Google Cloud ML Engine
Google has open sourced its ML framework – TensorFlow, which is used extensively inside Google; but for
more managed experience Google Cloud customers can choose “Cloud ML Engine”, so that Client
organisations need not worry about infrastructure at all and focus only on ML model.
Companies can use Google framework to build ML models or they can use the API’s directly.
54.
55. Machine Learning in Insurance
• “Anything You Can Do, AI Can Do Better” - George Argesanu, Global Head of Analytics from AIG
and Monika Schulze, Global Head of Marketing, Zurich Insurance Company
• Predictive analytics (Canonical problem “Regression”) using ML can be applied in all existing areas
of Insurance: Pricing, Fraud, Claims, Marketing, P&L Analysis, Behavioural Analysis, and
Preventive Insurance.
• As machines think faster and smarter companies will get more accurate pricing, processes will be
efficient, frauds are more likely to be caught, and losses will be more likely prevented.
• Insurance industry is going through disruption and will face new products: Telematics, Self-driving
cars, Internet of Things, and Cybercrimes.
• Insurance industry will have to think of business problems which will come in future and think
how machine learning will help to solve them: How fitness bands and apps change user
behaviour, how premiums are affected, how lives can be saved, and how possessions can be
preserved.
(see Notes)
57. Data Governance using Machine Learning
• The problems solved by ML are similar to data governance problems
such as dimension reduction, prediction, and regression.
• The commercial platform from Pedro systems is designed to make
sense of the unstructured data from unstructured documents such as
excel sheet.
• The Pendo data platform indexes the documents, searches data inside
documents and give insights, document’s full lineage, Iterates,
refines, and classifies the data.
• The ML algorithm indexes the documents, classifies the customer
data, and makes it accessible and searchable, full data lineage of
unstructured data
58. Reduce the Credit Gap - Individuals
• With the advent of mobile devices, internet of things, social media, and mobile payments
in past few years, more features are available for ML models.
• Measuring the financial well-being is a problem of Classification and Dimensionality
reduction.
• Traditional credit scoring system rely on individual traits – age, job, and gender – while
being oblivious to the spatial-temporal mobility patterns and habits of the individuals.
• Inspired by the emerging trend of mobile payment and geo-aware, a ML model build over
human consumption pattern across time and space is able to predict the financial well-
being by 30% to 49% better than comparable demographic models (Singh, et al., 2015).
• Inspired by the biological phenomenon of foraging, a basic pattern of animal movement for
food and resources, human purchase behaviour was studied through three shopping
behavioural traits: Diversity (Exploration), Loyalty (Exploitation), and Regularity (Plasticity).
Behavioural indicators were combined with more financial trouble indicators and ML
model was trained to predict whether customer is likely to have financial trouble or not.
59. Reduce the Credit Gap - SME
Traditional approaches for measuring financial
well-being such as Earning per asset or Equity
per asset have limited predictability.
One idea is to create a network of merchants
in which vertices represent merchants and
weight of the edge is based on the number of
common customers.
Analysing the network is the canonical
problem of Clustering (under unsupervised
learning) that is solved by ML. Structural
information, community detection, will be
used as indicator of financial well-being of
merchant. The information is used as feature
in constructing the next ML predictive model.
61. Data Governance
• Organizations preparing to adopt Machine Learning tomorrow should
start Data Governance today: either for one department or entire
organisation
• Stream data from silos/departments and invest in the use case, tie
the integration to create value – a progressive step to create
integrated platform of enterprise data for advance capabilities
(Wilder-James, 2016)
• To address the issue of data quality, Great Western Bank created a
data committee with members from different teams across
organisation. The team created standard definitions that teams across
bank use. (banktech, 2014)
62. Next Steps
• Find a business area where data is already robust and plentiful, and
has a business problem that can be solved by machine learning
• Train the managers, move to cloud, make organisation agile, find
business problems, finding use cases with robust data, apply data
governance, and prepare data today for applying Machine Learning
tomorrow
• Embrace the increased competition in data value chain and realize
ecosystem is the new value proposition
• Build platform instead of product
63. Cautions
• Though guarantees of Machine Learning may be only theoretical, but
things will be done better with machine learning – We are relying on
the promise and power of computers
• It will also be interesting to see how the Machine Learning works
together with some other key developments such as data protection
laws, identity thefts, secure systems, and distributed ledger.
• Expectations from Business leaders and managers is more now on
how they approach this new relation between humans and machines,
and they become more accountable and liable when data is shared
and decisions are taken by machines.
64. Conclusion
• Its time rethink strategy and make it iterative as machines become
better and better
• One can’t ignore the opportunity that computing power is increasing,
we are collecting more data, storage cost is reducing, and algorithms
are improving
• Not only implement Machine Learning but also interpret the model
• Upgrade the IT architecture and Data Governance practices
Ref - https://www.youtube.com/watch?v=NRYLPmy8V1k
AI is broad term for field in which human intelligence is simulated in machine. ML is term applied to systems learning from experience (data).
Ai has been area of interest since the term was originated 60 years back but what is different in this decade.
Sources:
Domingos, P., 2012. A Few Useful Things to Know about Machine Learning. Communications of the ACM, 55(10), pp. 78-87.
Brownlee, J., 2013. A Tour of Machine Learning Algorithms. [Online] Available at: http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/
Kaluza, B., 2015. What Kind Of Problems Can Machine Learning Solve?. [Online] Available at: https://www.evolven.com/blog/what-kind-of-problems-can-machine-learning-solve.html
For long, Banking is a standardized industry and Banks developed legitimate best principles because cause and effect relation was linear, repeatability allowed predictive models to be created, and the focus was on efficiency.
The decision model was: sense incoming information, categorize that information, and respond in accordance with predetermined practice.
The culture of Banks prevent Banks from being agile and innovative.
The slow adoption of latest technology can be related to the culture of banks.
Are experienced managers ready to be influenced by machines and delegate some of their tasks to machines without fear of being replaced?
Even though Banks have recognized the opportunity of Big Data, Data Science, and ML, they are still struggling with the today’s big data ecosystem, because of shortage of trained data scientists who can make sense of troves of data collected by Banks over years.
Not only Data Scientists, Banks also need skilled resources, Data Engineers and Architects, who can transform the legacy IT systems and enterprise architectures into ML ecosystem.
Banks have used Data warehouses for storing large data and ran queries over it, which worked well with structured data, but, in the age of massive unstructured data and big data, ecosystem needs upgradation.
Too many technologies and frequent changes is confusing.
Source: Fedyk, A., 2016. How to Tell If Machine Learning Can Solve Your Business Problem. [Online] Available at: https://hbr.org/2016/11/how-to-tell-if-machine-learning-can-solve-your-business-problem
Source: Fedyk, A., 2016. How to Tell If Machine Learning Can Solve Your Business Problem. [Online] Available at: https://hbr.org/2016/11/how-to-tell-if-machine-learning-can-solve-your-business-problem
Source: The Royal Society Staff, 2017. Machine Learning Report, London: The Royal Society.
Source: Galli, S., 2017. Machine Learning in Financial Credit Risk Assessment. [Online] Available at: http://www.datasciencefestival.com/soledad-galli-machine-learning-financial-credit-risk-assessment/?mc_cid=bc35d812cc&mc_eid=23f320c300
Source: Wenzel, A., 2017. Challenges & Opportunities with Data Science In Insurance. [Online] Available at: http://www.datasciencefestival.com/ansgar-wenzel-challenges-opportunities-data-science-insurance/
PayPal is receiving many transactions and labelling each one is not possible so idea behind Active learning is how to select minimum number or best sample (top 10 samples) needed to build ML models. A robust sampling technique is key for success of Active Learning. Idea is to select sample effectively that should be given to human for labelling, for example, suppose a model predicts if transaction is fraud or not with accuracy of 0.5, then sample can be given to human to label it correctly using other tools, so, rather than giving random datasets from huge pool of database give dataset that is closer to boundary (0.5 accurate), and once data is labelled by human, add that data back to model. Research says, randomly selecting sample and applying ML results in 70% accuracy but ML model using Active learning results in 90% accuracy. PayPal started with small set of labelled data, and analysed the current ML model being used. PayPal was using single layer neural network but they found deep learning and GBT successfully challenged their existing model and GBT outperformed deep learning because features were human engineered. One benefit of deep learning is it learns features so that is another research for PayPal to reengineer some of the features, which are human engineered, using deep learning. PayPal uses both models and if they are in disagreement then human experts intervene (Query by Committee). Humans are feed with data and supporting infrastructure such as simulation environment to re-compute the feature logic and apply it back to ML model. Finding and applying the benefits of deep learning is next logical step in ML for PayPal. PayPal is using H2O tool rather than TensorFlow and R language. 1-year data (11 million transactions) was used to train and tested on 4 million transactions using 500-600 features.
Improving the prediction can be done by analysing bigger data (uses transactions of more than 5 years), improving algorithm, better features, or better labelling.
Source: Click, C., 2017. Policing the Stock Market with Machine Learning. [Online] Available at: https://www.infoq.com/presentations/score-ml-stock-market
Organisations moving to cloud and using Google Cloud ML engine will have most of the work related to cleansing of data (estimated 90%). ML is the rocket and data is the fuel. The growth of ML acceptance is exponential as reported by NVIDIA that CPU sales are flat for past few years but GPU is where growth is, driven by ML. Google has realised this explosion and investing in special purpose hardware – Tensor processing unit (TPU). Currently Google is using ML in its broad range of products: Android, Apps, Gmail, Maps, Speech, Search, Translation, YouTube, Self-driving car, and so on. The use of ML in Google is transparent to end user but it is running behind, for example, when user searches for photos of beach the ML in background classifies each image if it is a beach photo or not. Google is providing the ML as an API (Application Programming Interface) such as Cloud Vision API, Speech API, Natural Language API, Translation API, Video Intelligence API, and so on to its clients. Benefit of using Google API’s is that they are already tested on huge data and are in production for few years, such as same technology used by google for image search is available for organisations to use, as an API. Lot of problems related to ML are solved by Cloud providers such as Google, except the data. Organisations have to think about data governance, cleaning, lineage, and quality. API’s related to image and video can be applicable to Banking industry such image uploaded by end user for insurance claims: Video Stabilization, Stabilize the video taken by phone; Protect the content from malicious uploads, such as, preventing end user from uploading the adult content; Classify the model/make of car from photos; Classifying the videos uploaded by end users; pulling text from image or masking the sensitive text in image such as ID number in image before storing in database; and finding the location of image. Another benefit of using the API’s related to images is that they have already learned the image recognition so clients don’t need to train the model. Combining Natural language processing API’s for speech recognition, language translation, Image, and Video API’s Banks can create truly immersive apps such as Face Banking, Video advisors, and revamp the existing online and mobile banking platforms. Using ML to build relationship graph of the information and data. Rather than hard coding rules for information retrieval and search (hard coded search queries with rules), use ML to learn the patterns in similar to RankBrain, which is a key signal used in Google search.
Google is experimenting to use AlphaGo, based on reinforcement learning, to reduce the amount of energy spend on air conditioning. Google reduced the data centre usage by 40% by using ML. Since, Google has lot of high-end solutions for both structured and unstructured data, analytics and big data tools, ML API’s and frameworks; it makes a promising use case for organisations to move their data to Google Cloud or any similar one, if not done or not in progress yet.
Many use cases from financial services companies are already running on Google cloud such as Risk analytics and regulations, Customer segmentation, Cross-selling and up-selling, Sales and Marketing campaign management, and Credit worthiness.
Insurance company, AXA, uses ML to calculate premium, shifting from rules based system to ML based system. Similarly, Bank SMFG, moved from rules based system of credit card fraud detection to ML based system on Google Cloud.
End users provide data, and ML learns from data to better understand user; thereby creating a new level of personalised services. Business processes can be streamlined as machines learn about the patterns and anomalies. Providing more relevant information to right user on right time.
Source: Ramanathan, R., 2017. Infuse your business with Machine Learning. [Online] Available at: https://cloudonair.withgoogle.com/events/next-live-emea-2017/watch?talk=session-3-day-1
Source: Argesanu, G. & Schulze, M., 2016. Anything You Can Do, AI Can Do Better, London: Insurance Nexus.
In the report prepared by George Argesanu, Global Head of Analytics from AIG and Monika Schulze, Global Head of Marketing, Zurich Insurance Company “Anything You Can Do, AI Can Do Better”, they say Machine Learning is a natural progression of more than century of predictive analytics and it is cutting edge technology that blows apart old thinking and catapult company into generating business even many times faster it has been before.
One of the biggest difference between the existing statistical modelling such as Generalised Linear Model and ML models such as Neural Networks and Decision Trees is that machines are algorithmic based and self-learning so they blur the line between past and present which predicting future whereas statistical models use historical data and parametric approach. ML models can be applied on customer by customer basis and can combine images and videos. Predictive analytics (Canonical problem “Regression”) using ML can be applied in all existing areas of Insurance: Pricing, Fraud, Claims, Marketing, P&L Analysis, Behavioural Analysis, and Preventive Insurance. As machines think faster and smarter companies will get more accurate pricing, processes will be efficient, frauds are more likely to be caught, and losses will be more likely prevented. Insurance companies face same challenges as discussed before in Literature Review chapter and they can modernise case by case. But, Insurance industry is going through disruption and will face new products: Telematics, Self-driving cars, Internet of Things, and Cybercrimes. Insurance industry will have to think of business problems which will come in future and think how machine learning will help to solve them: How fitness bands and apps change user behaviour, how premiums are affected, how lives can be saved, and how possessions can be preserved. Per line of business companies need to articulate what problem they are trying to solve and where is data. Create foundation of data for analytics, move data and computations to cloud, and engage machine learning. Insurance companies lack frequent interactions with their customers therefore they need to encourage customers to engage in machine learning process through physical technologies such as Telematics and through applications for greater access to data. Many insurance companies start with risk mitigation approach. Partnership within company and with external providers and taking it slowly are defining ways for approaching Machine Learning.
“Recognition pipeline in an autonomous car” is an example of two canonical problems of ML – Feature extraction and Classification – It is a 3-step process and ML methods are used. The image is captured in real time and classified as “Stop Sign”.
Insurance companies dealing with insurance of autonomous cars will face many use cases of classifications while possibly processing claims of autonomous cars, when they will analyse the images captured and decisions made by the autonomous car that met an accident.
Source: pendo, 2017. pendo systems. [Online] Available at: http://www.pendosystems.com/
Source: Singh, V. K., Bozkaya, B. & Pentland, A., 2015. Money Walks: Implicit Mobility Behavior and Financial Well-Being. PLOS ONE 10(8): e0136628.
Eagle, N., Macy, M. & Claxton, R., 2010. Network Diversity and Economic Development. Science, 328(5981), pp. 1029-1031.
Image Source: MIT Online course: Big Data & Social Analytics