Banking & Insurance Professionals
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Activity:
Question posted: 04 25 2008 23:00:05 +0000,
3 answers, 193 views, last activity
07 06 2010 20:18:08 +0000
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Sudhir answered this question very nicely. Besides the above, where Sudhir talks about credit / loan decision & risk assessment, I use predictive analysis in relation to retail banking in terms of exploring the bank's customer database, and based upon individual customer's transaction history develop churn models.That model gives me a probability of a particular customer leaving the bank in say next three months, six months and so on; so that appropriate actions could be taken in advance to prevent that attrition. I do not know the numbers in India, but in the US it takes roughly $200 or more to acquire a new customer, where as it takes less than $20 to retain the existing one. Building up on this cost figure, no wonder it is much more profitable to use predictive analysis in increasing the products per customer.
Hope this helps. Thanks.
Predictive analysis targeted at customer behavior is a commonly deployed aspect of such analytics, but it is only one specific application. There are other applications for predictive models.
Resource management: Analytics can help identify hot spots where resource constraints are likely to create an adverse impact on customer experience. Thus, such analysis may be used to plan remote ATM servicing cycles, peak-period and slack-period staffing plans, re-stocking plans for deposit tickets, order forms and informational brochures.
Capital Management: By predicting cycles in cash demand, assessing CD redemption & renewal patterns, loan repayments and deposit surges, it is possible to develop strategies that match fund inflows to fund deployment and ensure minimal "idle" capital.
Rate Management: By measuring price elasticities of various products and customer segments it is possible to fine tune the rate changes in variable rate products and maximize margins. Developing models that study competitive environment, macro- and micro-economic factors, it is possible to develop more sophisticated rate schedules that maximize returns from margin management.
Advertising & Marketing plans: Predictive models can also help build the case for specific marketing programs, advertising campaigns and sales activities, enabling fact-based decision making on marketing proposals.
Predictive models may come in many flavours - from the simple historical fact based models, heuristic and deterministic models, to logistic regression and cluster analysis. Different models may have specific applicability in specific contexts.
Hope this helps.
Predictive analytics is a dimension of business intelligence that allows organizations to assess both risks and opportunities. In retail banking, that process translates into questions such as: Which customers are likely to default on loans? Which are likely to be profitable, long-term customers? Getting the right answers is important because it has a direct effect on the bottom line.
To answer those questions, historic data is used to construct a model that correlates the characteristics of a group of customers with their financial behavior. From a large set of measurements, key influencing factors are identified. The same information is then collected about other customers or prospects and matched against a profile that correlates with the target behavior. Retail Banks can make decisions about issuing loans or marketing new products based on expected consumer behavior.
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