The invention relates generally to customer relationship management (CRM) and more particularly to a system and method for providing credit to members of a target population using an integrated business risk and marketing strategy.
There are a number of distinct analytical processes that consumer or retail finance organizations routinely undertake. Of major importance is the “risk” or “credit scoring” process in which customers are scored according to their propensity to remain in good financial standing and not default on obligations. The risk scoring process in general may be based on several factors, such as the customer's credit risk profile, his/her income, his/her profit potential, the offered product and the credit policies of the finance organization. Also of significant value is the computation of “response scores” for marketing campaigns, which are dedicated to identifying high-potential current or future customers, “high potential” being defined by a favorable likelihood of response of a consumer to a new offer of credit. A number of statistical analysis approaches have been used to define the characteristics that are most predictive of a consumer's future behavior.
Traditionally, banks and financial institutions have kept the risk management and customer relationship management functions as separate entities. The decisions that involve both are usually taken at a higher administrative level, often in an ad-hoc fashion. Risk management is traditionally based on identifying customers that have a propensity to remain in good financial standing and not default on obligations, or in other words, that have a minimum risk of default. On the other hand, customer relationship management is based on identifying high-potential current or future customers and may not necessarily have a low risk of default. However, in the current highly competitive consumer finance world, the need to market aggressively to moderate risk individuals and households can be business-critical. Therefore, conflicting goals between risk and marketing may often arise, resulting in a non-unified risk and marketing strategy.
Therefore, there is a need for a system and method that can leverage both risk and marketing aspects of a financial relationship. In addition, there is a need for a system and method that can serve to recommend business actions that can optimize both these aspects and provide an analytical framework for making collective decisions on routine processes such as pricing of a financial product and determining the creditworthiness of the members of a target population.
Embodiments of the present invention address this and other needs. In one embodiment, a method for integrating business risk and marketing objectives into a unified business strategy for providing credit to one or more members of a target population is provided. The method comprises collecting risk data and marketing data associated with the members of the target population and building a scoring model from the risk data and the marketing data by generating a set of risk scores and a set of marketing scores associated with the members of the target population over a range of credit that could be provided to the members of the target population. Then, the method comprises collectively using the risk scores and the marketing scores in a network model, wherein the network model generates a probability distribution of expected use of the credit over the range of credit that could be provided to the target population. Finally, the method comprises generating an optimized risk and marketing strategy for selecting the amount of credit to provide to the members of the target population based on the probability of expected use generated by the network model.
In another embodiment, a system for integrating business risk and marketing objectives into a unified business strategy for providing credit to one or more members of a target population is provided. The system comprises a database comprising risk data and marketing data associated with the members of the target population and a scoring model that receives the risk data and the marketing data from the database. The scoring model generates a set of risk scores and a set of marketing scores associated with the members of the target population over a range of credit that could be provided to the members of the target population. The system further comprises a network model and an optimization model. The network model collectively uses the risk scores and the marketing scores from the scoring model and generates a probability distribution of expected use of the credit for the range of credit that could be provided to the target population. The optimization model receives the distribution of expected use of the credit from the network model and determines the level of credit to offer the members of the target population in order to maximize a business measure subject to a set of business constraints.
These and other features, aspects, and advantages of the present invention will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein:
In a particular embodiment, the database 12 includes a risk database 14 and a marketing database 16. The risk database 14 includes risk data associated with the members of the target population. The risk data may include demographic data, transaction level data and account level data associated with the members of the target population. As used herein, “transaction level data” refers to data pertaining to transaction events such as debits; credits as well as failure events like missed repayments on the account through any channel. In particular, the risk data may include, information about a member/customer's job profile and his/her position held in the job, his/her credit history, the number of years of residence of the customer at his/her current address, his/her income statement, the bank accounts and the life insurance policies of the customer and the loan repayment history of the customer. One of ordinary skill in the art will recognize that the above examples are exemplary illustrations of the types of risk data that may be stored in the risk database 14 and are not meant to limit other types of risk information that may be stored in the risk database 14.
The marketing database 16 includes marketing data associated with the target population. The marketing data may include metrics for measuring and maximizing the profitability of the one or more customers/members of the target population. The metrics for measuring the profitability may include business measures such as, balance, income, contributed value, expected dollars of use, dollars of credit offered, and number of people receiving additional credit. The marketing data may also include business objectives/strategies for managing the existing customer base and strategies for expanding the customer base (such as, through channel strategies or product strategies). Again, one of ordinary skill in the art will recognize that the above examples are exemplary illustrations of the types of marketing data that may be stored in the marketing database 16 and are not meant to limit other types of marketing information that may be stored in the marketing database 16.
Referring again to
In a particular embodiment, and as shown in
The risk scores 24 and the marketing scores 26 are then input into a network model 28. In accordance with one embodiment, the network model 28 is represented by a Bayesian Belief Network (BBN), and will be described in greater detail with respect to
In a particular embodiment of the present invention, and as will be described in greater detail with respect to
The optimization model 40 receives the distribution of expected use of the credit from the network model 28 and determines the level of credit to offer the members of the target population in order to maximize a business measure subject to a set of business constraints. In one embodiment, the optimization module uses a mixed integer program to perform the optimization. Further, in accordance with the present technique, the credit offered to a member of the target population, may be derived based on several factors such as, the initial credit line, the repayment terms and the interest rates associated with the individual. Therefore, level of credit that could be offered to a member of the target population may result in an increase or a decrease in the credit amount to be offered to an individual. In a particular embodiment, the optimization model 40 optimizes the risk adjusted contributed value (RACV) subject to one or more business constraints 42 to arrive at a business decision 44. In accordance with one embodiment, the business constraints 42 include constraints on the total amount of credit available for the members of the target population, the interest rate, the total size of the target population receiving credit and the total allowable risk level. The business decision 44 may include a decision on the amount of credit that can be provided to the members of the target population based on the probability of expected use generated by the network model.
Following an appropriate business decision 44, a Campaign/Market Rollout 46 may be performed as a means to implement the business decision 44. The implementation may be through mass communication media, advertising, or by a display of the financial products. Customer Action/Behavior 48 may also be observed during the campaign/market rollout process 46. Observations from the customer action/behavior 48 may then be used to update the risk data and the marketing data stored in the database 12. In certain embodiments, the customer action/behavior 48 may also be used to update the risk model 20, the marketing model 22, and the nodes in the network model 28 or the business Knowledge/Practice 38.
In accordance with an exemplary operation of the BBN 50 of the present invention, the distribution for the Initial Credit Line (ICL) 56 for a member/customer from the target population may be determined as follows. Referring to
Referring to
The Action Credit Line (ACL) 66 and the Action APR (AAPR) 68 represent decision variables and are also processing nodes in the BBN 50. Decision variables have a special significance vis-à-vis other nodes in the network. Whereas other nodes are historical state nodes, decision nodes can be used to represent multiple scenarios or alternatives. As shown in
The outcome of the BBN 50 is a distribution of expected profit or returns from the use of the credit for the members of the target population over the range of possible credit. As used herein, the “expected use of the credit” refers to the amount of usage or credit that is expected, or the amount of annual return in terms of the interest paid to a creditor for each dollar of credit that is offered to a particular demographic distribution. In one embodiment, the output is represented by a business measure to be optimized. In a particular embodiment, the business measure is a risk adjusted contributed value (RACV) 70. The RACV 70 refers to the contributed value (a measure of profit from the credit) that can be generated from the members of the target population keeping in mind the risk factor associated with the use of the financial product and at the same time meeting the expected level of profitability from each customer.
The network model, developed in accordance with the present invention, collectively uses the risk scores and the marketing scores in a single framework to arrive at a unified business strategy. As will be appreciated by those skilled in the art, marketing objectives are based on identifying different ways to attract and acquire new customers through customer management strategies, channel strategies, product strategies, promotional strategies, retention strategies and reactivation strategies. These strategies focus on retaining existing customers and increasing good balance and interest income. On the other hand, risk objectives are based on establishing a company wide portfolio and decreasing poor balance through new credit line strategies, credit line strategies for existing customers and collection strategies. The network model, developed in accordance with the present invention, integrates both risk and marketing strategies into a single decisioning platform by the collective use of both risk scores and marketing scores within a single framework.
As will be appreciated by those skilled in the art, the embodiments and applications illustrated and described above will typically include or be performed by appropriate executable code in a programmed computer. Such programming will comprise a listing of executable instructions for implementing logical functions. The listing can be embodied in any computer-readable medium for use by or in connection with a computer-based system that can retrieve, process and execute the instructions. Alternatively, some or all of the processing may be performed remotely by additional computing resources based upon raw or partially processed image data.
In the context of the present technique, the computer-readable medium is any means that can contain, store, communicate, propagate, transmit or transport the instructions. The computer readable medium can be an electronic, a magnetic, an optical, an electromagnetic, or an infrared system, apparatus, or device. An illustrative, but non-exhaustive list of computer-readable mediums can include an electrical connection (electronic) having one or more wires, a portable computer diskette (magnetic), a random access memory (RAM) (magnetic), a read-only memory (ROM) (magnetic), an erasable programmable read-only memory (EPROM or Flash memory) (magnetic), an optical fiber (optical), and a portable compact disc read-only memory (CDROM) (optical). Note that the computer readable medium may comprise paper or another suitable medium upon which the instructions are printed. For instance, the instructions can be electronically captured via optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
While only certain features of the invention have been illustrated and described herein, many modifications and changes will occur to those skilled in the art. It is, therefore, to be understood that the appended claims are intended to cover all such modifications and changes as fall within the true spirit of the invention.