Upskilling in Analytics, a must for Risk Management Professionals — KnoGrow
By: Saikat Halder
Tags: Upskilling, Risk Management, Knogrow, Analytics

In the field of Financial Risk Management, the most important upskilling need is knowledge of Analytics. Modern risk management demands upskilling in Analytics. This is a must if you wanted to be groomed and known as a successful risk manager. Data science and analytical know-how are the risk management tool across the industry today. If you can not upskill your knowledge in Analytics, soon there is a chance to be identified as a redundant resource. Hence knowledge in data science skills is a must for a risk manager’s roster.

Please visit my previous blog to more about upskilling is the professional lifeline, I have mentioned the importance of upskilling for professionals.

Why Analytics is so important in Risk management?

Analytics extensively using data for its outcome. In previous decades risk management was majorly driven by industry experts, who were using their age-old experience and observation power in decision making. That process was predominantly qualitative with a higher percentage of chance factor. Gradually with more usage of database management and digitization in the financial industry, data capture mechanism has made a quantum leap from hard copy based manual record keeping to computerized digital record management which can be assessed, searched, and maintained easily in a cost-effective manner.

Since data capture has been more digitized and automated, the pool of data also started giving more insights on certain facts that were earlier in the dark. Perception-based risk assessment has a higher risk of mistakes and hence always goes with a potential of higher loss. New age data capture has opened the scope of data science to flourish with the advanced error-free outcome. The advent of data management and Analytical software made it easy to manage a large pool of data in quick time and gives specific need-based outcome.

Types of analytical approaches used in the risk management world.

There are two types of risks most prevalent in the financial industry.

Financial risks are mainly related to the cash flows of financial institutions, which are directly associated with financial loss and connected with the profit and loss account of the organization. On the other hand, the non-financial risk is majorly dealing in reputation risk, legal risk, and hidden capital losses which may affect financially the organization. For a typical financial institution following risks are categorized as financial risks.

Credit Risk/ Cash Flow Risk

Market Risk/ Trading Risk

Operations Risk

Counterparty Risk

Following risks are categorized under non-financial risks.

Risk from Fraudulent Activities

Money Laundering Risk

Risks from Misrepresentation of Facts

Breach of Contracts

Conduct Risk

Cybersecurity risk

Compliance risk

Regulatory risk

Reputational risk

As I have mentioned above, data are the major raw material for the application of analytics in risk management. For financial risk analysis, information on financial data, cash flow data, maturity information data, past default data, market movement data, and asset position data is being used. Analytics can be used in two ways like portfolio analysis and the development of forecasting models. In a typical portfolio analysis of financial risk, financial institutions used to perform analysis to get the possible risky asset classes, the potential losses against current asset positions. Contribution of top loss-making assets in the enterprise level loss estimation and strategies based on the outcome especially for those stocks, assets, or corporates where future investment decision to be taken.

For non-financial risk approach, past transaction records, origin and sources of funds, the intensity of interaction with geography-specific regulations, independent Audit outcome and further deep dive, hacking sensitive product identification and support decision for cybersecurity are performed. Where Analytics performs data cleaning, data sufficiency testing, outlier analysis, data management to make it ‘query language’ friendly. Data science also identifies potential offenders and helps an organization to report those cases to the regulator within the timeline.

Analytical skills of high demand

Basic knowledge and understanding that is required in analytics are quant skills and knowledge of statistics. If you wanted to develop a career in risk analytics, you must know statistical rules and approaches. In addition to that following skills are also in demand.

Knowledge of Analytical software (SAS, R, Python, etc.)

Understanding and expertise in big data management

Business understanding to perform sense check of the output

Management Information pack and presentation skill for senior management reporting

So, if you are an individual and wanted to develop your career in risk management and Analytics, these skillsets are the primary requirement for your upskilling plan. Furthermore, an analytical mindset with an interest in detailing is the most desired character that risk managers are looking for.

Comparison in risk assessment approach — Pre and current Analytics era

As I mentioned earlier, pre analytics risk assessment was purely expert-driven. So past knowledge of the industry, performance of certain flagship companies, number of years the experts covering assets of a particular sector, past observations, etc, plays a major role in decision making.

Let us take an example of traditional credit risk assessment done by experts, where five major components are evaluated named Character, Capacity, Collateral, Capital, and Condition. All these components of potential customers are evaluated by experts through their year-long experience along with some simple financial estimations. If the customer can effectively cross certain threshold values, the case can be marked as a pass, and the facility gets sanctioned.

To get more details please refer corporatefinanceinstitute web site.

Multiple level deep dive, what-if analysis, assessment of cross-functional data, analysis on likelihood through correlation, etc were not possible to perform in earlier approach. Pre analytics approach was mostly top-down based on certain recommendations. However, Analytics based data-driven approach is more bottom-up and realistic. A lot of simulations are performed against multiple scenarios to test the effectiveness of the approach. Following steps are performed using analytics to assess credit approval cases for a customer.

Assessment of historical performance

Assessment of demographic data

Assessment of income and liability status

Assess the score of the customer against internal data and the data acquired through application and mapped it against bureau score.

Finally, assign a default probability and map against certain pricing recommendation

Validation of the output with test datasets

If all parameters are passed, funding recommendations are communicated.

Changes that Analytics has made in Financial Risk World

Data science has changed the entire decision-making process in the risk fraternity. The integration from data capture to the final recommendation stage has made the entire risk management value chain more sophisticated. Today risk management is not a separate section/division in the organization that does its work in a silo, rather data-driven risk management is a culture in the organization where all sections are equally participating. Gone those days where customer level information is collected in a register manually and experts depend on their understanding and gut feeling in assessing a decision.

Analytics today design target specific products based on risk profiling of the customers. A promotional campaign for a new launch is entirely driven by data analysis. Regulators not only been reported on capital and risk assessment information, but financial institutions are also sharing the steps to arrive at multiple decision points and output of stress test outcomes

Where Analytics stands today?

Advanced analytics and Machine learning approaches are on the card to dominate the entire strategic landscape of Financial Institutions. Massive changes are happening in the data capture platform from customers. The more institutions go digital more demand for correct data and hence more demand in Analytical skillsets.

Now it is obvious that if you wanted to develop your career in financial risk management, you need to upskill yourself in Analytics and Data science requirements. The industry is moving towards fast-paced decision making where credit/investment decisions are conveyed within 2–3 hours or even less than that. Massive automation initiatives are being taken by the world’s leading financial institutions using Analytics at the center point.

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