Developing a strong collections unit requires clearly defined, documented and consistent policies and procedures that guide staff through the collections process and instruct them on how to respond in particular situations. Such policies and procedures should include a variety of strategies.
Establish Client-Contact Policies
When should the bank make initial contact? Is the best way to contact the client through telephone, email, printed letter or visits? The key to selecting the best method is weighing the costs and benefits of each available method based on number of days past-due and the probability of total debt collections. Contact policies may include preventative strategies, such as a payment reminder, and should include a plan defining dates of future contact and the steps to be taken in the collections process.
Any financial institution faces an enormous volume of decisions that must be made every day. In collections, we must decide when to contact the client, who should contact him, how to approach him, what product to offer, how to deal with broken promises, how to deal with lost or missing clients, what to do in cases of tragedies or natural disasters, and many other decisions that cannot be entirely delegated onto the experience of a loan officer. Risk-based collections strategies provide valuable tools for the decision-making process.
Implementing a risk-based collections strategy requires at least the preparation of the following:
- A thorough review of the external information available regarding regulatory environment, limitations, competition, the target market, etc;
- The design of the databases to support the construction of risk-management reports for monitoring and for development of tools aiding collections decisions;
- Training for staff responsible for risk management and collections strategy definitions;
- Definition of the tool for calculating the level of risk the client presents, whether that client is recoverable, and the best strategy for recovery.
The first tool for identifying the probability of default of a client is data mining. Alberto Teskiewicz defines data mining as: "The process of identifying significant correlations, patterns and trends that are hidden among the wealth of information in the database, through the combination of statistics, mathematics, and recognition of patterns. It is an interactive process that allows the institution to convert data into knowledge, generating benefits that translate into lower costs and higher income."
While many institutions currently consider data mining vital to portfolio evaluation, it is still not used as an important tool to maximize the effectiveness of collections activities. Data mining allows lenders to forecast the probability of recovery and provides them with a score useful in prioritizing past-due loans based on recuperation probability.
Another tool available for risk management of collections is the “Collections Scorecard,” a system for assigning points based on the characteristics of a client in order to obtain a numeric value that reflects how likely this client is to default relative to other individuals. It is important to note that a collections scorecard does not indicate how much risk you should expect; instead, it indicates how a particular loan is expected to perform relative to other loans.
After segmenting clients based on their probability to repay, the lender develops a set of strategies for collecting from the different segments, while optimizing its financial, human and infrastructure resources. The success of risk-based collections depends not only on the development of the score used to forecast repayment probability and the optimized collections strategies, but also on the appropriate implementation and monitoring of the defined strategies.
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