Predictive analytics in financial service
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1) High performance business computing enables organizations to analyze vast amounts of internal and external data using cost-effective systems and analytical methodologies. 2) Techniques like decision trees, machine learning, and text analysis are used to analyze customer, demographic, and financial data to identify patterns and predict outcomes. 3) Modeling approaches like ensemble modeling and uplift modeling analyze multiple scenarios and models to identify the most effective predictions and treatments.
Predictive analytics in financial service
- 2. FACTORS FOR HIGH PERFORMANCE BUSINESS COMPUTING (HPBC) • Explosion of data becoming available, both internal and external, to organizations • Availability of cost-effective and accessible systems (in terms of computational speed, data storage, memory) to be able to do something useful with it. • Methodologies to analyze and make sense of this vast amount of data are being developed and improved every day
- 3. DATA: Customers by identifying exactly who they are Real estate holdings Their net worth Where they live Demographics and more Lifestyle Spending patterns Income
- 5. MODELING : • NET LIFT (OR UPLIFT) MODELING : where two or more scenarios are analyzed simultaneously to trace all possible outcomes and choose the right treatment (or lack of treatment) for a particular situation • ENSEMBLE MODELING : in which a suite of models are run and the final response comes from a weighting of the individual models’ results, and where the model-weighting can also be refined based on the situation.
- 7. HYBRID FRAUD FRAMEWORK PROCESS FLOW :
- 8. CASE STUDY 1: Money laundering is a serious problem in the financial industry. For example, here is segmentation to clusters that indicates a suspicious cluster based on the collected data (Fig.1). This information helped a financial institution to see the abnormal patterns and check them to reveal money laundering. Besides money laundering there are many other financial crimes such as credit card fraud, insider fraud, mortgage fraud, insurance fraud and other. Taking this into account, a financial institution must have a high-performance risk management system. This can be achieved with sophisticated algorithms of Predictive Analytics and Machine Learning.
- 9. Contd…. Fig.2 is a clustering example of the dependence between different clients' categories and their bank activity. This analysis helps to define clients with anomalous behavior which can potentially indicate fraudulent activity.
- 10. CASE STUDY 2 : 1. Developed statistical models on historical data to predict charge-off/ early pay-off and other loss producing behavior propensity. 2. Developed an easy to use MS Excel dashboard tool with the model algorithms built in. 3. The tool takes an application’s profile details as input and gives scores to it on various risk propensities. 4. Based on the scores, the tool also categories the applications into Low Risk/ Medium Risk and High Risk segments
- 11. Contd……….. 1. The business has an informed way to accept/ reject applications based on what behavior to expect. 2. By using the recommendations to improve risk profile, they have a way to improve the portfolio quality without increasing rejection rates. 3. Charge-off rates have gone down by 9% (with no change in rejection rates) within six months of implementation.
- 12. HELPS IN: Saving time and money by allowing to focus on top prospects Identifying new prospects with connections to customers Maximizing business development success with standard and custom training Applying data-driven strategy to all marketing efforts Segmenting and ranking order customer base Finding new prospects that mirror most profitable customers
- 13. BENEFITS TO FIRMS : MARKETING •To boost cross-sell and upsell revenue because sales and marketing staff can use predictive models to anticipate the needs of customers •Product Introduction •Trigger offer recommendations based on life events, such as closing on a home or the birth of a child FORECASTING •Churn/Attrition Management •Forecasting •Derivatives Forecasting •Credit Scoring •Collection Strategy Optimization RISKMANAGEMENT •Fraud Detection & Prevention •Securities Pricing Product Purchase Propensity •Risk Modeling & Analysis