Financial Industry - Data Mining Services for Financial Institutions
There are numerous ways Business Intelligence Solutions can help your financial services institution improve its business performance and processes, reduce expenses, increase revenue and gain a competitive edge.
As an example, see our case study “Data Mining Approach to Credit Risk Evaluation of Online Personal Loan Applicants” presented at Fifteenth (2009) ACM SIGKDD International Conference on Knowledge Discovery and Data Mining in Paris, France.
Definition of data mining and detailed characteristics of a data mining project can be found in our white paper “Data Mining: the Means to a Competitive Advantage”.
In order to understand why traditional statistics is not adequate tool for vast majority of data driven financial industry problems, see our presentation "Data Mining vs. Statistics".
The essence of our approach is to understand and analyze our client’s business problem and corresponding data through the prism of dissimilar statistical/data mining models. As a result we are always able to produce the best possible model /results and help our clients in the most effective and scientifically sound way.
Consumer Credit Risk Management
Development of new, or improvement of current, procedures of risk identification, quantification, measurement, and utilization, including Basel II compliance and beyond. Optimization of overall verification methodology, credit scoring/behavior scoring modeling (product default scoring, customer default scoring, product profit scoring, customer profit scoring, response scorecard, usage scorecard, and attrition scorecard). Analysis of consumer bankruptcies and delinquencies (e.g. assessment of probability of first payment default, likelihood that a consumer will miss three consecutive payments in the next 12 months, or likelihood that a consumer will not have defaulted within a predetermined number of months from the account opening date). Risk-based pricing and price optimization for any given risk input.
Our case study “Data Mining Approach to Credit Risk Evaluation of Online Personal Loan Applicants” was presented at Fifteenth (2009) ACM SIGKDD International Conference on Knowledge Discovery and Data Mining in Paris, France.
Debt Management
Customer repayment behavior analysis, debt scoring, determination of debt portfolio pricing, identification of different debtor segments (e.g. self-cures, debtors with a high propensity of rolling over to a higher delinquency bucket, debtors with a high likelihood of becoming charge-offs). Improvement of recovery rates and determination of appropriate actions and timing in various debtor segments.
Customer Relationship Management
- Gaining, sharing, expanding, and utilizing knowledge about customers.
- Customer profiling, segmentation, and clustering (see our presentation “Cluster analysis vs. Segmentation”) with the usage of demographic, geographic, psychographic and behavioral variables and historical business information.
- Data enrichment of the internal customer database with Census and other public/private data (the essence of data enrichment and different sources of external supplementary data for CRM problems are discussed in our presentation “Data Enrichment for CRM”).
- Improving marketing ROI by targeting specific customer segments with personalized campaigns.
- Identification of early adaptors. Customer profitability and customer loyalty. Customer acquisition and retention. Churn modeling/customer defection and customer attrition. Customer satisfaction, identification of unsatisfied customers, and development of the program to improve customer satisfaction. Personalized marketing and pricing. Targeted marketing and multichannel management. Optimal (the most profitable) allocation of your marketing funds.
- What-if scenario analysis. Detecting undeserved customers and unprofitable
customers and developing program to increase sales. Customer scoring according to a likelihood of defection, likelihood of response, likelihood of a product purchase, likelihood of being a good customer (see our case study “Data Mining Approach to Credit Risk Evaluation of Online Personal Loan Applicants”.
- Developing lifelong relationships with your most profitable customers and addressing other CRM challenges like up-sell and cross-sell (application of bivariate regression to up-sell and cross-sell is described in our white paper “Join Regression Model for Sales Analysis”)
Customer Knowledge Management
Gaining, sharing, expanding and utilizing the knowledge residing in customers (e.g. through survey development and analysis of combined primary and secondary data).
Creating, supporting, and analyzing two-way flow of value for both customers and financial institution on a regular basis (knowledge repository development and support). Co-creation of a product/service with the customer in order to generate value for both parties and to achieve competitive advantage (e.g. what is the next best product or service for this customer that will increase value for both the customer and the organization?)
Design and Execution of Specific Campaign
Targeted marketing to optimize marketing effectiveness, customer acquisition, cross-selling, up-selling, retention campaigns, etc. Customer win-back campaign (recovering customers lost to competitors). Pilot study design (e.g. price test to estimate customer sensitivity to savings account rate increase). Event monitoring (e.g. when certain pre-defined thresholds and targets are reached in a customer's account) and event prediction.
Credit Card Risk Management
Credit card scoring, identification of clients with high probability of abandonment, fraud detection (model development to generate early warning signals of possible fraudulent transactions).
Purchase Card Data Analysis
Identification of misuse of public funds and fraud detection, monitoring the purchase pattern, quantification of risk of fraud associated with a purchase card transaction.
Analysis of Financial Status of Firms
Prediction of financial health of firms, firm classification according to their financial health, prediction of bankruptcy, identification of financial distress patterns.
Analysis of Transactional Data
Association/Market Basket Analysis.
Web Analytics
Website analysis, predictive analysis and predictive modeling of website user behavior (e.g. prediction of user propensity to convert, buy or churn), analysis of website
statistics and key performance metrics, measuring content effectiveness, discovering user segments and navigation patterns, customer satisfaction with web experience, uncovering areas of opportunity for financial institution websites to more effectively support sales, share of wallet and customer loyalty.
Impactful e-mail marketing. IP address geocoding of website visitors to: identify geographic clusters of current and potential customers; examine the efficiency of advertisement as a function of geography; determine locations of future advertising campaigns, etc. (in order to get insight into spatial dependency and spatial regression, see our presentation “A Brief Introduction to Spatial Regression”)
