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Regulatory Credit Risk Management: Improve Your Business with Efficient CRM

Regulatory Credit Risk Management: Improve Your Business with Efficient CRM

In the aftermath of the Great Recession and the credit crunch that followed, the financial institutions across the globe are facing an increasing amount of regulatory scrutiny, and for good reasons. Regulatory efforts necessitate new, in-depth analysis, reports, templates and assessments from financial institutions in the form of call reports and loan loss summaries, all of which ensures better accountability, thus helping business initiatives.

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Also, regulators have started asking for more transparency. Their main objective is to know that a bank possesses thorough knowledge about its customers and their related credit risk. Moreover, new Basel III regulations entail an even bigger regulatory burden for the banks.

What are the challenges faced by CRM Managers?

  • Sloppy data management – Unable to access the data when it’s needed the most, due to inefficient data management issues.
  • No group-wide risk modeling framework – Banks need strong, meaningful risk measures to get a larger picture of the problem. Without these frameworks, it becomes really difficult to get to the tip of the problem.
  • Too much duplication of effort – As analysts cannot alter model parameters they face too much duplication of work, which results in constant rework. This may negatively affect a bank’s efficiency ratio.
  • Inefficient risk toolsBanks need to have a potent risk solution, otherwise how can they identify portfolio concentrations or re-grade portfolios to mitigate upcoming risks!
  • Long, unwieldy reporting processManual spreadsheet based reporting is simply horrible, overburdening the IT analysts and researchers.

What are the Best Practices to fight the Challenges Noted Above?

For the most effective credit risk management solution, one needs to gain in-depth understanding of a bank’s overall credit risk. View individual, customer and portfolio risk levels.

While banks give immense importance for a structured understanding of their risk profiles, a lot of information is found strewn across among various business units. For all this and more, intensive risk assessment is needed, otherwise bank can never know if capital reserves precisely reveal risks or if loan loss reserves sufficiently cover prospective short-term credit losses. Banks that are not in such good shape are mostly taken under for close scrutiny by investors and regulators, as they may lead to draining losses in the future.

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Adopt a well-integrated, comprehensive credit risk solution. It helps in curbing loan losses, while ensuring capital reserves that strictly reflect the risk profile. Owing to this solution, banks buckle up and run quickly to coordinate with simple portfolio measures. Fortunately, it will also lead to a more sophisticated credit risk management solution, which will include:

  • Improved model management, stretching over the whole modeling life cycle
  • Real-time scoring and limits monitoring
  • Powerful stress-testing capabilities
  • Data visualization capabilities and robust BI tools that helps in transporting crucial information to anyone who needs them

In summary, if your credit risk is controlled properly, the rest of the things are taken care by themselves. To manage credit risk perfectly, rest your trust on credit risk professionals – they understand the pressing needs of decreasing default rates and improving the veracity with which credit is issued, and for that, they need to devise newer ways and start applying data analytics to Big Data.  

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SAS and Equifax Clouts Deep Learning and AI to Improve Credit Risk Analysis

SAS and Equifax Clouts Deep Learning and AI to Improve Credit Risk Analysis

The noteworthy triumphs over us, humans, in Poker, GO, speech recognition, language translation, image identification and virtual assistance have enhanced the market of AI, machine learning and neural networks, triggering exponential razzmatazz of  Apple (#1 as of February 17), Google (#2), Microsoft (#3), Amazon (#5), and Facebook (#6). While these digital natives command the daily headlines, a tug of war has been boiling of late between two ace developers –  Equifax and SAS – the former is busy in developing deep learning tools to refine credit scoring, and the latter is adding new deep learning functionality to its bouquet of data mining tools and providing a deep learning API.

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Banking Business and Banking Instruments-3: Mortgages

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In this blog we discuss the final banking instrument- Mortgages, for which models are developed extensively. A mortgage is a debt instrument, secured by the collateral of specified real estate property that the borrower is obliged to pay back with a pre-determined set of payments. Mortgages are used by individuals and businesses to make large real estate purchases without paying the entire value of the purchase upfront.

 

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Mortgages are mainly of two types: (a) Traditional Mortgages (b) Adjusted Rate Mortgages.

 

Traditional Mortgage is a fixed rate mortgage, where the borrower pays the same a fixed rate of interest for the life of the mortgage. The monthly principal and the interest payments never change from the first payment to the last. Most fixed rate mortgages have a 15-30 year term. If the market interest rate rises, the borrowers’ payment does not change. If the market interest rate drops significantly, the borrower may secure the lower rate by re-financing the mortgage.

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Credit Risk Analytics and Regulatory Compliance – An Overview

Credit Risk Analytics and Regulatory Compliance – An Overview

 

Post the Financial Crisis of 2008, there has been an increase in the regulatory vigilance of the capital adequacy of commercial banks across the globe. Banks need to be compliant with different regulatory capital requirements, so that they can continue their operations under situations of stress. A majority of analytical work in Indian BFSI domain is to provide analytical support to US based multinational NBFC’s. We would like to throw some light on the opportunities and scope of credit risk analytics in the US banking and financial services industry. The Federal Reserve requires the banks to be compliant with three main regulatory requirements: BASEL- II, Dodd Frank Act Stress Testing (DFAST) and Comprehensive Capital Analysis and Review (CCAR).

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