How Analytics Has Transformed Risk Management -Knogrow.com

Post the 2008 subprime crisis, risk management has transformed tremendously. Analytics has played a key role in this transformation. Multiple areas where Analytics has changed the risk management approach are to be discussed in this article. I shall try to highlight 5 major areas of how Analytics has transformed the risk management framework.

1. Analytics transformed Insurance underwriting.

Application of actuary has increased in many folds in Insurance underwriting. The usage of statistical tables, life expectancy rates, natality & mortality percentages below and above a certain age, probability graphs of health deterioration amongst a group of people, and many more are often being considered during the finalization of insurance pricing.

With advancements in computing capabilities, data analytics have expanded beyond the boundaries of traditional actuarial science. The application of analytics opens new windows of opportunities for actuarial science professionals. With the help of analytics applications, the insurance premium assessment becomes dynamic today. The power of analytics and increased accuracy ratio over claim possibilities has made the sector more lucrative.

Based on Artificial intelligence (AI), insurance companies creating innovative and exotic products to satisfy the needs of their customers. More national and international players are offering multiple products just because the profitability expectation has increased. Data warehousing, management of big data has completely changed the pattern of how customers are being evaluated.

In 2020, the pandemic has shaken the entire world and its effect is still nibbling on the economy of countries. The conventional what-if analysis model has proven not sufficient to analyze the impact of Covid-19. Advance use of predictive modeling with the help of AI & ML is crucial to analyze the damage that insurance farms have already faced. Post-bankruptcy of AIG during 2008, the regulators become more stringent on capital maintenance for insurance companies through solvency norms.

2. Financial crime, how analytics has transformed the assessment

Financial institutions were suffering historically from multiple financial crimes, which may be in the form of corruption, terrorist financing, money laundering, fraudulent activities, fund diversion, insider trading, and sanction breaches. Billions of funds are getting siphoned out illegally from the system for certain individual’s interests at the cost of common depositors.

Natural language processing, image processing, sentiment analysis, blockchain technology, encryption techniques, biometrics, robotic process automation, and big data management has helped financial institutions to reduce non-financial risk losses to a significant extent.

Multiple machine learning techniques and advanced software like Python, Statistical Analysis System (SAS) have made it faster enough to trace fund movements and tracking down high-value transactions on a real-time basis. Natural Language Processing (NLP) techniques, image processing and biometric have reduced fraud and impersonation cases to a considerable extent. With the help of Analytics, financial institutions continuing to close the loopholes to safeguarded depositors’ funds.

3. Loan and Asset products of banks

The most remarkable changes created by Analytics are in the bank’s loan and asset management process. The lengthy, qualitative assessment of loan proposals is obsolete today. Turnaround time for loan proposals has come down to some hours only. Product design, customer profiling, and customer risk assessment have completely become automated.

Analytics has revolutionized the process by developing dynamic customer scorecards, risk-based customer profiling, potential loss estimation for each onboarded customer, and risk-based dynamic pricing. Earlier experts-driven credit scrutiny has been replaced by product-specific statistical models.

The application of machine learning techniques has increased significantly in retail and specific wholesale exposures. Data-driven insights help financial instructions to make better business choices.

4. Product development strategy

Starting from financial institutions to marketing organizations, telecom enterprises to pharma companies, everywhere, target customer-based marketing is the trend of the new strategic era. Analytics has played a bigger role in this target marketing approach.

The analysis of customer behavior reorganizes product buckets according to the spending pattern, requirement of lifestyle necessities all captured and analyzed to offer the customer the most required combo. This approach of product designing, and business strategy has reduced promotional costs to a considerable extent and has increased the strike rate of conversion. Market intelligence, risk profiling, precision marketing, and behavioral analysis are all possible due to the incremental importance and adoption of analytics in business.

5. Analytics in digital transformation risk management

Exponentially digital marketing creates new challenges for operations risk management. Enterprises from multiple sectors are adopting digital marketing channels to directly reach the customers and get their instant feedback to plugin lapses. Digital marketing has certain areas of risks that may magnify losses. Like payment portal/ bill desk, hacking of customer information, cyber-attack over company servers, hacking of bank accounts, and many more.

Analytics today using Machine Learning (ML) techniques and Artificial Intelligence (AI) can simulate scenarios and estimate potential losses. It also can identify the loopholes in the monitoring process and hence can help to take precautionary measures before actual incidence takes place. Dynamic security layering, biometric screening, algorithmic security coding are some measures adopted to protect sensitive data and collaterals of digital marketing.

Future trends in risk management

Analytics has changed the risk management landscape completely. With the advancement of data capture means and techniques, processing of those data in meaningful ways has gained importance. Data mining and analytics filled the gap. The advancement of technology and infrastructure has initiated various kinds of risks in the business world. With immense data mining capability and super-fast computational power, analytics has emerged as a flexible weapon to counter non-traditional risks. Following are the trends going to be seen in the risk world.

• Risk management will be complementing business as a productivity enhancer — Business and risk strategic conflict will erode gradually. You can refer to my blog at knogrow.com “Does Risk Management Strategy Have A Conflict with Business Growth?” to understand more about business and risk management strategy. There will be a balancing approach on both.
• Dynamic regulation will be driven by innovation — Risk is traditionally regulation-driven, however with the rapid change of platform and technology innovation will drive future business and to cope with the change regulator will also change the guidelines. Hence constant upskilling of knowledge and applicability will be the key to survival. You should refer to my blog on “Upskilling in Analytics, a must for Risk Management Professionals” to understand more on the demand.
• Business sustainability and the status quo will be challenged by disruptions — The business world is changing amazingly fast. So, strategies must be accommodative. Disruptions like ecosystem change, change of technology, and business model will challenge cash flow constantly. Risk managers will play a pivotal role in cash flow maintenance.
• Behavioral science to dominate business decisions — people psychology, preferential pattern, mental makeup, and many more questions will drive business leaders to find answers through risk management eye. Business growth is hiding behind those questions and who will be able to crack at the beginning will be the market leader. Analytics has full potential to explore.
• Real-time risk management — usage of AI and ML techniques will be at its peak to gauge and manage risks on a real-time basis. New generation risk will not allow giving time to formulate and frame strategy. It must be proactive and preventive. Therefore, multifaceted approaches are to be synchronized properly with business to form a business risk combo to offer to the customers.

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Through this article, I have tried to frame the potential of Analytics in the risk world. There would be many more with the change of time and approach. For young professionals, it would be a good referral source to decide their career aspirations.

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