Integrated approach to credit and fraud risks in credit portfolio management

By Subramanian Venkataraman and Sasidharan Chandran

Context and Purpose

Credit risk is defined as the risk that a counterparty will fail to perform fully its financial obligations, and can arise from multiple activities across sectors. For example, credit risk can arise from the risk of default on a loan or bond obligation, or from the risk of a guarantor, credit enhancement provider or derivative counterparty failing to meet its obligations. Fraud is an intentional act or failure to act to gain an undue benefit.  Amongst the numerous frauds that can hit credit portfolio of a bank, focus of this paper is on nonpayment of amount owed when they fall due despite being in a position to pay.  Generally, banks use separate models for credit and fraud risks.  Many banks include some fraud risk related factors as part of their rating models. The Purpose of the article is to investigate linkages between credit and fraud risks in credit portfolio and suggest an approach to building an integrated approach to deal with them in an effective manner.

Parameters of Credit Risk

Credit risk is a combination of closely related but different dimensions, as depicted in the following figure.

Figure 1: Credit Risk Dimensions
Figure 1: Credit Risk Dimensions

The default, nonfulfillment of obligations by borrowers to banks is the most prominent risk factor in credit portfolio. If a commercial credit obligation is at least 90-day past-due (90 DPD), then, the borrower is considered to be in default according to the regulatory standards; even when an exposure is not past due or has been past due for less than 90 days, banks have to apply due diligence to decide if the borrower is unlikely to pay the obligations or not.

Default can arise due to the following two reasons:

  • Inability to pay – reflects credit risk and it can be due to external (macroeconomic and industry) or internal factors (financial, business and management factors)
  • Unwillingness to pay – manifestation of fraud risk since borrower has the ability to pay. This is generally reflected by management factors and may be triggered by other factors.

PD rating models for commercial lending and their interpretation

Banks build credit risk rating models for each credit risk parameter and use them extensively for risk assessment, regulatory capital computation, risk based pricing, monitoring and control of risks.

Using financial and non-financial factors, PD rating model classifies borrowers into various rating grades.  The best rating grade has the least PD and the worst rating grade has the highest PD.

When rating is low (PD is high),

  • Borrower would be impacted by both borrower-specific and external factors. In such cases, the borrower is exposed to both credit and fraud risk.
  • Credit risk can be ascertained by analyzing borrower performance ratios, transaction details, credit risk mitigants etc
  • Fraud risk is assessed through qualitative factors, namely, management factors
  • Idiosyncratic risk factors could contribute to both credit and fraud risk
Figure 2 – Risk factors in commercial credit risk rating (non-exhaustive)
Figure 2 – Risk factors in commercial credit risk rating (non-exhaustive)

When rating is high (PD is low),

  • Impact on the borrower would be high due to external factors such as macroeconomic and industry.
  • Borrower is expected to have stable idiosyncratic risk factors, hence credit risk is reduced though not completely removed due to macro economic risk factors
  • Incorporating stress scenarios to the credit risk model will strengthen reliability of the models and borrowers crossing the hurdles in such stressed scenarios would be expected to overcome adverse economic conditions
  • Fraud risk can be assessed by looking at management factors
Figure 3 – Interaction between credit and fraud risks
Figure 3 – Interaction between credit and fraud risks

Given the linkages between credit and fraud risks, it would be beneficial for banks to integrate credit and fraud risk models.  One of the good starting points is to clearly identify root cause of each default and attribute it to either of the risks.  This will pave the way to build an unbiased, integrated dataset that can be basis for building credit portfolio models. 
Towards an integrated approach

Bio – Subramanian
Dr Subramanian Venkataraman is a Senior Consultant in the Risk  Management Practice of Tata Consultancy Services’ Banking and Financial Services Business Unit.  He drives initiatives in the areas of credit risk, conduct risk and other allied areas.  He manages research and competency development for the group.  His risk consulting experience revolves around ERM, credit risk, market risk, stress testing, model validation and risk-adjusted performance management.  He has developed a number of solutions for banking clients and authored point of views.

Bio – Sasidharan 
Sasidharan Chandran is working as a Banking and Financial Services (BFS) Risk Management Consultant with Tata Consultancy Services (TCS). Currently, he is associated with the Risk Management Practice and has contributed significantly to the innovative analytics and cognitive solutions in the space of risk and regulatory compliance. His experience revolves around credit risk, conduct risk, BCBS 239, Basel regulatory programs, forbearance and non-performing exposures reporting.
Sasidharan holds a Bachelor of Technology degree from NIT, Kurukshetra and Master’s degree from S P Jain School of Global Management