Data Analytics is helping banks in reducing their risk exposure, cutting down on customer acquisition costs and extracting better profitability from the existing customers. One sure sign of the fact that analytics is here to stay is the fact that many banks are now setting integral customer analytics cells. And it is not just restricted to MNC banks like Citibank and Standard Chartered who are emulating the best practices of their parents abroad but also homegrown banks like ICICI Bank. Analytics service providers such as Fractal Analytics has developed several predictive analytics based models for credit risk management, cross-sell, customer retention, customer segmentation etc.One of the oldest area in which banks have been using analytics to great results is credit scoring.
Statistical credit score-cards serve up as a better alternative to the traditional judgmental methods of risk appraisal when a bank is making a decision whether to lend to a customer or give him a credit card or not. Risk Scorecards combine historical loan default data with the demographic and transaction details to arrive at a risk score for an applicant. Statistical techniques are applied to data on existing customers to generate equations that can accurately distinguish good customers (customers who repay on time) from bad customers (customers who don’t repay on time or don’t repay at all). This equation or scorecard is used to score new applicants. Statistical scorecards lend themselves to automation. From the consumer’s point of view this ensures quick turnaround time in the evaluation process as well as total consistency, eliminating any bias, which may be present in a human analyst.
Another instance of analytics in banking and where results are apparent almost instantaneously is cross-selling. Banks are leveraging their existing databases of customers more judiciously to rope in customers for lending products like credit cards and loans. Since banks are sitting on wealth of information like liability transaction which sets the base for response models predicting their response to another marketing offer. The cross sell models throw up interesting triggers about the customer setting the stage for life-cycle based marketing or event based marketing. Already, close to 70% of credit cards portfolios of most banks are sourced through cross-sell from their own bank account customers.
One crucial fallout of analytics based marketing campaigns is the tremendous cost savings accomplished by the bank by restricting its soliciting efforts to the customers who are predicted to be active rather than widening its efforts onto the entire customer base and incurring huge costs there. Using customer segmentation solutions, a bank can get 1.5 times more eventual customers to a particular offer while actually contacting a much narrower customer base.
Nowhere else the effect of analytics based marketing is more apparent than in the credit cards companies where analytics have become a way of life. In a fiercely competed battle for wallet share where an average credit card holder holds 3 to 4 credit cards and free credit cards have become the norm, getting a credit card customer to spend on your credit card and ensuring that he sticks to your credit card. Analytics based customer marketing and value management solutions will help you to design optimal customer development strategies, maximize your customer’s profitability by widening the relationship across different banking products and optimize existing customer relationships.
Customer Segmentation strategies which help a portfolio manager to know smaller cohesive groups sitting within his larger customer base, understanding their transaction patterns and hence preempting his requirements goes a long way towards customizing campaigns, offers linked to campaigns and even the tone of the communication directed towards the customers.
Tags: Analytics for Banks, Credit Risk Analytics, Credit Risk Management, Credit Scorecards, Credit Scoring, Cross-sell, Cross-sell models, Customer acquisition, Customer Analytics, Customer Retention, Customer Segmentation, Customer Value Management, Fractal Analytics, Predictive Analytics, Scoring Models, Scoring Solutions
