Overview of Data Analytics in Lending Business
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The document discusses the evolution and application of AI/ML in the finance and banking sectors, including analytics in customer lifecycle management, credit scoring, and product offerings from financial institutions. It outlines industry use cases for AI, the tools and techniques relevant for data scientists, and the challenges faced in adopting AI technologies. Additionally, it emphasizes the importance of data management, regulatory compliance, and the emerging fintech landscape in transforming financial services.
![Problem
statement
Technology is
core Solve
problems
using
analytics.
Financial inclusion
and alternate data
FinTech or financial technology has
emerged as a relatively new industry
in India.[1]
FinTech is an industry comprising
companies that use technology to offer
financial services. These companies
operate in insurance, asset
management and payment, and numerous
other industries.
Fintech:
What is a
Fintech?
Changing face of Finance Industry](https://image.slidesharecdn.com/analyticsseminar-200516060527/85/Overview-of-Data-Analytics-in-Lending-Business-21-320.jpg)
Overview of Data Analytics in Lending Business
- 1. Sanjay Kar Analytics in Finance Domain Banking Focused 9th May 2020 Email: kar.sanjay.k@gmail.com
- 2. How AI/ML have evolved and the different use of AI technology. The recent trends and industry use cases Analytics overview 01 Understand the different financial institutions and the product offerings. Industry Overview 02 Use of analytics in customer lifecycle management. Understand customer life cycle and decision points where analytics is used today Use of Analytics in Banking 03 What to read? What are the tools and techniques you should know to get into banking domain. What is the expectation from a data scientist? How to Prepare yourself for Analytics job? 04 Agenda
- 3. Analytics Journey ( AI/ML) The incredible journey of the field so far
- 4. https://soundcloud.com/openai_audio/jukebox-86115728 https://www.youtube.com/watch?time_continue=182&v=LY7x2Ihqj mc&feature=emb_logo Can read , write, sing and paint https://openai.com/blog/competitive-self-play/ https://twitter.com/cyrildiagne/status/1256916982764646402 Can drive, play , cut paste https://www.youtube.com/watch?time_continue=47&v=mJDf1GXaF- w&feature=emb_logo Can Think, give advice Artificial Intelligence use cases
- 5. AI Timeline Chatbot • Chatbot capable of conversation with human being • SAS institute Robot Sony launches AiBO pet Dog Initial release Scikit Learn in 2007 Python Most used programming language AI John MaCarthy introduces the term Artificial Intelligence Deep Blue Deep Blue defeated great chess champion Garry Kasparov Alexa Amazon launches Alexa Alpha Go 20192014-182000-201419971960-901956 Alan Turing Test If a machine tricks Human in thinking its human then machine has intelligence 1950
- 6. Analytics - Industry use cases Retail E- comm Manufacturing BFSIHealthcare Highly used • Supply chain analytics • Segmentation and customer targeting • Market basket analysis • Loyalty programmed • Dashboarding • Campaign management • Churn Analytics AI & Analytics • Recommender system • Next best offer • Inventory management • Marketing analytics • Reporting • Forecasting Analytics : Medium • Clinical trial data analysis • Disease pattern analysis • Drug discovery and development • Epidemiology modeling AI: High • Robotics • Maintenance prediction • Process automation • Demand forecasting • Warranty reserve analytics AI : Medium • Credit risk modeling • Pricing analytics • Customer service • Automation • Alternate data • New to credit analytics • Fraud & Collection
- 7. Analytics in Banking Industry
- 8. Type of Financial Institutions Banks, NBFC’s , MFI’s Retail banking Private Banking Investment Banking Group Loans Small Finance Banking Payment Banks, All insurance related companies Health Insurance General Insurance companies Insurance brokers Third party providers Financial Technology companies • Technology and platform provider • Collection house • Aggregators • Alternate Data • Analytics company • AI companies All type of Investment companies • Wealth Management • Mutual Fund house • Broker • Traders Banks FinTech's Insurance Investment
- 9. Banking Products Only lending products is shown here. Savings account is banking product but not a loan Loan to buy a house or renovate a house. The interest rate is lower. LTV is key term. Foreclosure if the loan is not paid. Home Loan/Mortgage To buy the vehicle. It is a depreciating asset and hence the risk is more Car Loan Low risk product. Loan against FD Cash Credit, Line of Credit, Overdraft etc. Some of the loans can be partially secured Business Loan Loan to meet the personal need such as marriage, children education, travel and any other need. Personal Loan Plastic with a credit limit. The interest rate is generally higher Credit card Secured Un-secured Un-secured loan credit risk is higher compared to the Secured loan as there is higher recovery in case of delinquent and NPA. The interest rate for secured loan is generally lower compared to un-secured loan
- 10. Customer Life Cycle Management Targeting Target the best customer who are more probable to take the products Portfolio Management Manage the customers and loans to reduce the losses and increase the revenue Fraud Identify and reject the fraudulent transactions and application Origination Approve the customers which are more like to give profit. Process automation Collection Collect the loan amount from the delinquent customer without incurring much expense Regulatory Adhere to the regulatory requirement of loss provisions, Basel norms and other Indian Accounting standard
- 11. Definition Credit score is a numerical score range to measure the credit risk of customers. The higher the score better is the customer. Features Techniques used to create and validate credit scoring models include: • Logistic regression and linear regression • Machine learning and predictive analytics • Binning algorithm (i.e., monotone, equal frequency, and equal width) • Cumulative Accuracy Profile (CAP) • Receiver Operating Characteristic (ROC) • Kolmogorov-Smirnov (K-S) statistic Areas it is used Credit approval, Cross sell and up sell What is a Credit Score? The measure of Credit Worthiness
- 12. Predictive Model Development Historic Vintage selection Snapshot Period selections Independent Variable and Target Variable Variable Reduction Binning & WOE Model Fitting, Validation Independent Variables: Demographic Data: Qualification, Income, House owner Bureau Data: # of delinquency in past 6 month, Total balance, DBR, Vintage in bureau, Payment% Dependent Variables: 90+ DPD in 12 month 60+ DPD in 9 month
- 13. Example1: Underwriting Strategy Approving the Marginal population which are profitable for business with higher interest rate If the customer doesn’t pay, it is called delinquent customer. After missing 4 payment , its called 90+DPD. Bad Good definition Post manual and STP process Final Approval 80% 10% 20%
- 14. Cut-off Strategy Historic Time period sample creation Target Variable creation Parameter selection along with score Strategy Development/ Back testing Apply the strategy at current snapshot Strategy Optimization KRI/KPI identification Approve/Decline Bad Rate <10k 10-50k 50-1lakh >1lakh <600 20% 17% 13% 8% 600-750 15% 12% 5% 4% 750-850 10% 8% 3% 2% >800 2% 1% 0.8% 0.2%
- 15. Propensity Model Behaviour risk score Cross sell strategy Example: 1. Selling personal loan to existing credit card customer 2. Selling a top-up loan to existing Personal Loan customer Example2: Cross-sell Upsell Strategy
- 17. Challenges in adopting AI https://www.equifax.com/videos/introduction-neurodecision/ Top Reasons: - ROI - Lack of expertise - Black box - Regulatory requirement - Hype vs reality - Lack of data - Lack of data engineer - Use of Jargon
- 18. Terminology in Lending Business Approval Rate STP Delinquency NPA/DPD Roll Rate Reage Revenue NIM/RAM Vintage Analysis Foreclosure Recovery Application and Behavior score to predict the future credit risk or delinquency behavior. Generally a supervised ML model with classification technique used A-score/B-score A business input based simulation problem to come up with the optimum pricing model Risk Based pricing To predict the propensity of the customer to buy or take certain product. It an again a classification problem with some time multinomial logistic model /Neural network is used Propensity Model Business calculation to arrive at the business value using the score cut-off and back testing of the information Swap-in/Swap-out Regulatory modeling categories. The segmentation methodology is used before using logistic model to predict the PD. LGD and EAD uses regression technique. PD,LGD,EAD, Basel Validate the model with respect the accuracy such as KS, AUC, Gini, stability such as PSI, CSI and rank ordering Model Validation Banking Terminology
- 19. CIR Stores, manage and infer the data Sell data to the participating institution needs Governed by RBI. Can’t share data without customer consent Credit Bureau is a custodian of credit data. All the financial institutions share their data to the credit bureau. • There are four Credit Bureau in India • Equifax, Transunion (CIBIL), CRIF, Experian • Equifax is the only bureau with Retail, Commercial, Employment and MFI bureau What is a Credit Bureau? What is a Credit Bureau? Making the credit available to deserving
- 20. Service provider Provides information of various products Generates Leads Collection Customer can apply for loan, Insurance and many other services There are many websites which allow an user to view loan offers from multiple banks and lenders and also apply for a loan to one of these lenders. These are known as loan marketplaces or loan aggregators. Example: Paisa bazar ,Credit Mantri ,Bank Bazar , Policy Bazar What is an account aggregator? What is an aggregator? Serving customer needs real time
- 21. Problem statement Technology is core Solve problems using analytics. Financial inclusion and alternate data FinTech or financial technology has emerged as a relatively new industry in India.[1] FinTech is an industry comprising companies that use technology to offer financial services. These companies operate in insurance, asset management and payment, and numerous other industries. Fintech: What is a Fintech? Changing face of Finance Industry
- 22. Thoughts to grow.. How do you evaluate credit worthiness for a customer who haven’t taken any loan yet. Specifically customers not having digital presence
- 23. Tools & Technique used in Financial Industry
- 24. Tools used in Banking Analytics
- 25. Techniques used in Banking Analytics Linear Regression Logistic Regression Neural Network Random Forest XG Boost Multinomial Logistic Regression Lasso/Ridge Regression Decision Tree Supervised Clustering K-means Clustering PCA /Co-relation Association Analysis Unsupervised NLP Deep Learning Markov Chain Model Computer Vision RPA Blockchain AI & Recent technique Time Series ARIMA ARIMAX Moving Average Polynomial Smoothing Exponential Smoothing
- 26. Jobs in Banking Analytics
- 27. Type of jobs in Banking Analytics This is a subjective division of different roles in Banking Analytics Type of jobs Create models using ML & AI. Good at coding and statistics. Good at NLP and data preparation technique. Data Scientist Good at coding, data base management, technical architect. Good at model deployment and production. Testing, Cloud knowledge Data Engineer Create solution and describe the problem statements. Good at understanding analytics technique, use case and business knowledge. Can also develop models and strategies Business Analyst Tell a descriptive story. Good at coding and visualization tool Visualization/Reporting Good at articulation, understanding problem and providing solutions. Good in PPT skill Consultant
- 28. Path to Become a Banking Analyst • Pick one language among SAS, R or Python • Lots of data cleaning, unstructured data and Proficient in Coding Proficient in Business concepts Proficient in Data preparation Statistics, Visualization, Model Implementation • Product Knowledge • Revenue /Loss matrices • Terminology • How to make data ready for model • Snapshot period, Sampling, Bad definition, Target variable • Derived variables, Monotonicity • Github • Model implementation • API framework • Scala • Cloud • Plumber/Flask • Types of graphs • Create story
- 29. Resources Statistics Books Website or Courses02 Python Coding books Machine Learning Books Websites • "Statistics for Machine Learning" by Pratap Dangeti • Practical Statistics for Data Scientists by Peter Bruce and Andrew Bruce) • Core Python programming by Dr. R. Nageswara Rao • Python Data science Handbook by Jake Vanderplas • Hands-on Machine Learning with Scikit-Learn & TensorFlow by Aurelien Geron • Data mining & Predictive Analytics by Daniel T. Larose, Chantal D. Larose • Github, Kaggle, Towards Data Science, Analytics Vidya,Academia
- 30. Q & A