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

1. Machine Learning
in Banks
By,
Abhishek Upadhyay

2. Will Machine replace You? Simple Answer is “NO”

3. Machine
Learning:
Intersection of
programming,
statistics, and
data

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.

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

10. Machine Learning
Introduction

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

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

19. Challenges
In adoption of Machine Learning

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?

24. Culture

25. Skills

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.

27. Overcome
the Challenges

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

29. Visualize the
data

30. Visualize the
ML Model
and decision
making

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?

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

41. Use Cases
Existing few use cases of machine learning

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)

46. Fraud prevention in PayPal – Payments

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.

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)

56. (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.

60. Conclusion
Further work & Next Steps

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

65. Thank You
Abhishek Upadhyay