Traditionally, we design processes without any specific variations due to key user’s personality traits. We optimize process activities using LEAN and other techniques to reduce waste and increase efficiency. We all focus on a defined (successful) outcome for our customer needs. However, we had an opportunity within some of the recent projects to see and measure the influence of PersonalityTraits (typically from employees and customers) on optimization results.
Customer Behaviour Prediction Analyses was main focus in optimization of cross and up-sell procedures in a Bank. Based on more than 40 variables and thousands of business rules we predictCustomer Behaviour (for every individual customer and its contract) and “on–line” optimize process activities to achieve the best results for Bank.
Eye-tracking as a UX technics was used in HealthCare project to increase the quality and reliability of doctor’s decisions, measuring the time to perform the activity and corresponding proportion of incorrect or incomplete decisions. Based on the findings, personalized styles of UX components were proposed in key activities.
Anxieties have enormous influence on employee behaviour, usually resulting in strong defences and looking for opportunities to protect their positions. During the step-by-step process reengineering (inUtility Management Company) employee performance deviations (average, expected) were followed and corresponding level of automation was incorporated into processes at each step.
With increasing competition to retain the market position companies can achieve next level of providing services only with the respect to Personality Traits of Both, the Employees and Customers.
Customer churn has become a big issue in many banks because it costs a lot more to acquire a new customer than retaining existing ones. With the use of a customer churn prediction model possible churners in a bank can be identified, and as a result the bank can take some action to prevent them from leaving. In order to set up such a model in a bank in Iceland few things have to be considered. How a churner in a bank is defined, and which variables and methods to use. We propose that a churner for that Icelandic bank should be defined as a customer who has not been active for the last three months based on the bank definition of an active customer. Behavioral and demographic variables should be used as an input for the model, and either decision tree or logistic regression used as a technique.
Presentación que resume las actividades que se han llevado a cabo desde el sector de jóvenes de la Acción Católica General de la Diócesis de Getafe durante el curso 2011-2012
Customer churn has become a big issue in many banks because it costs a lot more to acquire a new customer than retaining existing ones. With the use of a customer churn prediction model possible churners in a bank can be identified, and as a result the bank can take some action to prevent them from leaving. In order to set up such a model in a bank in Iceland few things have to be considered. How a churner in a bank is defined, and which variables and methods to use. We propose that a churner for that Icelandic bank should be defined as a customer who has not been active for the last three months based on the bank definition of an active customer. Behavioral and demographic variables should be used as an input for the model, and either decision tree or logistic regression used as a technique.
Presentación que resume las actividades que se han llevado a cabo desde el sector de jóvenes de la Acción Católica General de la Diócesis de Getafe durante el curso 2011-2012
Mitigate the Impact of Great Resignation with AI Digital Assistants.pptxEmagia
Mitigate the Impact of Great Resignation with AI Digital Assistants.pptx
https://www.emagia.com/resources/ebooks/mitigate-the-impact-of-the-great-resignation-with-ai-digital-assistants/
Process Improvement for Pabit SolutionsSnehal Datta
This is a case study on Pabit Solutions Inc. to find out the reason for customer dissatisfaction during various service and on-boarding processes. Using DMAIC (Define, Measure, Analyze, Improve and Control), that is, a data-driven improvement cycle we have improving, optimizing and stabilizing business processes and designs.
Mitigate the Impact of Great Resignation with AI Digital Assistants.pptxEmagia
Mitigate the Impact of Great Resignation with AI Digital Assistants.pptx
https://www.emagia.com/resources/ebooks/mitigate-the-impact-of-the-great-resignation-with-ai-digital-assistants/
Process Improvement for Pabit SolutionsSnehal Datta
This is a case study on Pabit Solutions Inc. to find out the reason for customer dissatisfaction during various service and on-boarding processes. Using DMAIC (Define, Measure, Analyze, Improve and Control), that is, a data-driven improvement cycle we have improving, optimizing and stabilizing business processes and designs.
Fraud detection is a popular application of Machine Learning. But is not that obvious and not that common as it seems. I'll tell how QuantUp implemented it for WARTA insurance company (a subsidiary of Talanx International AG).
The models developed gave between 10% and 30% of reduction of losses. The project was not a simple one because of the complex process of handling claims and using really rich dataset. The tools applied were R (modeling) and DataWalk (data peparation). You will learn what is important in development of such solutions in general, what was difficult in this particular project, and how to overcome possible difficulties in similar projects.
The dynamic and fast paced nature of the global financial markets necessitate the use of technology to provide the industry with next generation solutions that are reliable and function with accuracy. The industry faces the challenge of providing innovative products to cope up with the demands of a growing population of technology-savvy and affluent clientele and at the same time increase their profitability by making effective use of resources with a view to minimize costs and risks with a dynamic workforce.
Financial institution is facing tougher competition, reduced margins, rapid change and new regulatory requirements, banking organizations are under pressure to have access to and provide more timely and accurate information. In order to overcome these challenges, ERP helps banks become more customer-centric and efficient. Critical business tasks – including business analytics, financial and accounting processes, human capital management, support and logistics – are improved for employee access, while customers, vendors and partners can gain more flexible, yet secure, access to key service areas. These enable banks to provide innovative, result-oriented and cost-effective solutions.
Bearing this in mind, we hereby present our proposal as attached for development, installation, implementation and maintenance of Enterprise Resource Management (ERP) for your organization.
Hisplus Systems Limited
o enable a superior customer service experience in the multi-channel environment services and communication must be consistent and identical offers need to be made available to the same customer via different channels. Banks that can achieve this can expect a higher customer engagement. To make this happen Banks (Issuers) need to re-look at their operating models to make it leaner and more meaningful for their customers. Effectively, the times are a changing and the Banks need to keep up with changing playing field.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...
Do we all react in the same way? Influence of People’s Personality Traits on process optimization?
1. ANDREJ GUŠTIN
Do we all react in the same way?
Influence of people's personality traits on process optimization
2. Andrej Guštin is a cofounder and CEO at CREApro, a
leading Slovenian consulting company focused
comprehensively on business process management
and innovation.
Vice president of IIBA CHAPTER SLOVENIA since 2009
4. Case background - story
• In 2009, a young boy died in a hospital, due to a (potentially) operational mistake.
• It was assumed, that the doctors overlooked some critical indicators in a Blood Lab Test
(BLT) and did not react promptly.
• Processes in hospitals were digitalized with deployment of EHR (electronic healthcare
record) and HIS (hospital information system) some years ago and it seemed that GUI and
UX might also be part of the operational risk.
5. Diagnostic process – From need to value
• Need: how to read the document and get the information 100% correct.
• Stakeholder: doctor, patient.
• Context: dynamic and stressful working environment in the emergency
department at hospital clinics.
• Change: design is important for humans.
• Solution: improved user experience with better graphical design.
• Value: decrease the average time needed to extract the information from the
document and increase the reliability of human activities.
6. Blood Test Results– EHR and Paper copy example
Original paper based BTRDigital presentaton of BTR
7. Why we used Eye tracking?
• How we really see things?
• Do we see them equaly?
• What are the natural patterns of reading?
• How can we take those facts into consideration ?
8. The experiment
• In the first (top) scenario information was
presented with a tabular view (like on the BLT),
• In the second (bottom) scenario we redesigned
the appearance to a more graphical, judicious
view.
• All test users got the same „problem
description“ and performed the same
procedure.
• During the test they were isolated, not to
communicate with each other.
• 24 people were included in the experimental
workflow.
10. The results – average time and distribution curve
30s
Source: https://books.google.si/books/about/Uporaba_interaktivne_ve%C4%8Dpredstavnosti_v.html?id=zM4GmwEACAAJ&redir_esc=y
12. Case background – the story
• Since economic crises in 2008, Slovenian banks
have been deeply involved in the collection
process due to the increased quantity and
volume of overdue outstanding receivables.
• Operational efficiency optimization led them to
decrease the number of employees, so
collectors were overloaded with tasks and
documents.
Growth of non-performing loans
Decline in the number of employees
13. Recovery process – From need to value
• Need: how to optimize collection process and increase the volume and amount of
collected payments.
• Stakeholder: back-office, customer service, call center, clerk, middle management
• Context: economic situation, as described
• Change: from human to machine decision making.
• Solution: predictive model (R) for probability calculations. Selectively targeting the
right debtors with the right collection strategies at the right time was proposed by the
Solution and integrated processes.
• Value: optimal allocation of resources to maximize the amount collected while
minimizing collection costs.
14. Predictive Model Development
15
Model
Algorithems
Cursors
Rules
Historical data Machine learning Result
New data for processing The calculation of probability for
delayed payment
Result
Model
DevelopmentDailyusage
What is the probability, that this Customer will
be late with this payment? Probability!
15. Behaviour cursors for predictions
Some cursors, used in the model:
x2: The amount of the credit approved
x9: The total amount of remaining part of the credit
x10: The number of days from credit approval
x11: The number of days to payment maturity
x13: were the delayed receivables in the previous year paid
x14: The date of the first delay
x15: The amount of the first delay
x16: Late payments in the past year
x19: The maximum number of days of delay in payments in previous year
Main decision tree and key cursors with their weights
16. Results – graphical presentation
The graphs below present a distribution of 2 cursors from 192 observed cases.
The left graph presents the result of the predicted model. Black dots are payments that
won‘t be paid.
The middle graph presents the same sample after the invoices were actually paid (or late).
The right graph presents the difference.
The model incorrectly predicted 3 cases out of 192, that is 1.5%.
This is much better than the collectors can do, even knowing their customers well.
18. How we see the results?
• We used survival curve to present
the results.
• We improve the calculation of the
profitability of the client
(controlling profitability per
customer).
• Cost calculation of collection and
recovery proceedings (against
potentially recovered value).
• Assessment of future debt
servicing capabilities.
• The calculation of the probability of
default of existing and new assets.
90 days
9% in number
20. Case background - story
• Back in 2010, a utility management service company started a process-reengineering
project with the main goal to increase efficiency and reorganize back-office services
as part of digital transformation.
• The head of the back-office was also a managing director and partner in the
company.
• After some successful pilot processes optimization, we redefined their main core
process.
21. Billing process – From need to value
• Need: increase efficiency and refocus on customer.
• Stakeholder: back-office, accounting and finance department, IT, customer
service, call center, middle management, senior management
• Context: economic situation, digital transformation, internal change of
workplaces and job positions.
• Change: 100% automatization of core process, focus on customer service.
• Solution: deployment of BPMS solution with tight integration with ERP and DMS.
• Value: reorganization of the work, customer centric approach.
Source: http://www.dlib.si/stream/URN:NBN:SI:doc-CPFDANEE/23ff0ac4-1c72-4398-a037-833bdff2c573/PDF
http://dsi2011.dsi-konferenca.si/upload/predstavitve/Mened%C5%BEment%20poslovnih%20procesov/Gustin_Andrej.pdf
http://dsi2010.dsi-konferenca.si/upload/predstavitve/mened%C5%BEment%20poslovnih%20procesov/gustin_andrej_upravljanje%20poslovnih%20procesov%20kot%20odgovor%20na%20sedanjo%20krizo.pdf
22. The Change Curve (developed by Elisabeth Kubler-Ross)
Source:https://www.linkedin.com/pulse/change-curve-tim-crocker
23. Process 1: processing of incoming document
• The first steps in process
optimization went smoothly.
• In a time period of four months
(1) we were nearly halfway to
achieving our goal.
• Normal deviation in the declining
time trend (moment at A) – some
ideas doesn‘t work .
• Prompt reaction and process
change led to expected results
(2).
• Size of the bubble presents the number of
documents
1
2
Real BPMS data from 2010-2013
24. Process 2: processing of contracts and invoices
• A small process change resulted in a
high deviation in employees’
performance (moment “C”).
• The primary cause of this was
employees’ anxieties of losing internal
business “power.”
• Top management and HR started an
internal campaign and promotion for a
retraining program.
• Step-by-step automation that finally led
to a nearly complete computer-
automated process (a final level of 98%
automatization).
• Size of the bubble presents the number of
documents
Real BPMS data from 2012-2015
Re-check
Re-work
Exceptions
Irrational
Incorrect
Senseless
This case I Named it…
I will present each Case in the same structure – first i will tell a short Backgoriund, than I will use IIBA core concept model BACCM™ to explain the real Business need, Solution and Value.
Finally, you will see the results of the optimization and the corelation to specific personal traits.
First Case is from Healthcare area…
The investigation revealed, that the doctors overlooked some critical indicators in a Blood Lab Test
One specific value was over the allowed value and they didnt see it.
Izpostaviti vprašanje = raise the question …
Point out the specific context of that case.
Working environment in the emergency department at hospital clinics is really dynamic and stressful.
Predpostavili smo = We have assumed that the average time to review and analyze the BTR is about 30 seconds…
Just to have a idea, how the BTR looks like in reality…
We used eye tracking cameras to analyse how people real „“read“ the BTR
As you might know, there are three typical ways how we read things – lets say one A4 page.
We assume that F patter is one used by doctors to read the BTR – but we did the experimet…
Graphical elements to point out the important differences and deviations in values.
Adjusted to the environment and the specific users…
Banking environmet case…
to cope with a significant increase of non performing loans….
Collectors were dealing with growing wolrkload on one hand and less time on the other hand…
Indirectly all other department were involved too
Zrcaliti čez črto… mirrore across the line
Invent a lot of exaptions and special cases, that were not supported by automated process and needed the human manual work to be processed
We react promptly, analyzed the cases, defined some now business rules and costs sharing keys and it was back to normal again…
Fear to go outside the office and work with the cliencs …