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Importance of Credit Risk Management

Increasing competition forces organizations to give more credits to both their existing and new customers. Covid-19 caused an increment in the complications and uncertainty of the future. Due to this, banks and other financial institutions are under immense pressure to design credit risk management systems that give faster results with high precisions in decision making. Accessing the creditworthiness of customers is one of the top priorities of risk managers so that firms don’t end up bankrupt.

Credit Risk Management Services

As seen in some countries, low interest is eating the profit margins. It is even negative at times, which implies that the savings are going down, resulting in a growing credit book as the only available solution. Central banks are also easing borrowing conditions in their respective countries. However, recovery from bad loans is still the bank’s responsibility.

Importance of Credit Risk Management Techniques :

In this environment, designing an efficient credit scoring system is one of the best credit risk management techniques a credit institute can do. Risks have to be managed by introducing new clients to the portfolio and finding new markets for expansion, keeping fraudulent customers’ identification in mind. Risk scorecards are widely used in banks and financial institutions for multiple purposes like acquiring new customers, maintaining a risk profile of existing customers, calculating expected losses on a portfolio, collections, and recovery management. In the past, due to the high costs of the scorecard building exercises, these credit risk management models were only affordable by big companies, however, with the introduction of new open source technologies designing, implementing, and maintaining these credit risk mitigation tools is low complexity and high priority project.

The following points should be considered before the development of any scorecard:

  • How reliable is the data? Data is the first building block for any decision-making engine. It should be error-free and assumption-free. Always remember garbage in, garbage out.
  • Assumptions – The calculated risk is the key, speculation is the lock that you won’t be able to unlock.
  • Existing solutions – Check what the existing credit risk management solutions are available with the organization. There is no need for redesigning the wheel if a workable solution is already available.
  • Regulations – Credit is one of the most regulated industries in the world. One should always be updated with the regulations in the industry.
  • Quick implementation – In this ever-changing world, designing solutions that will take years to build is of no use. Solutions must be built, tested, and implemented as quickly as possible.
  • Easy Interpretation – Black box solutions are hated by everyone. One should be able to understand and explain the models easily, simply, and lucidly.


Scorecards should be based on a strong foundation of robust data and well-tested econometric methodologies. Technology should be used to implement new ideas and to make a User Interface which can be used even by people without the technical knowledge to know details about the complex architecture. The design of the framework should be modular so that changing parameters impacting the model can be easily updated. Dynamic data plays an important role in fine-tuning the performance of the models. For loans with a longer maturity, like mortgages, updating the application level is important. Using the salary of a customer for a loan that originated 10 years back is pointless.

At Trugo Consultancy Services analytics division, we believe that analytics is for anyone who wants to tell their story through data. If a solution can be delivered by simple spreadsheets, we do it. If there is a complex situation where we don’t know the results, we explore. We always try to leverage the business knowledge of key stakeholders and try to bridge the gap of organizations through data, analytics, and technology.

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