Reactive to Proactive Operational Risk (OR) Management

Globally Banks are trying to make their operational risk management framework more forward-looking. Banks should seize the opportunities today’s advanced tools and vast data pools make possible. Predictive analytics techniques, machine learning, and artificial intelligence can help efficiently build and mine large and complex data sets that combine traditional Basel operational risk data with other data sources, including transaction data, non-transaction data, and external data- Recent perspective shared by Deloitte.

Many Banks started using AI/ML in customer service/engagement (Chatbot), cybersecurity, fraud detection, credit scoring and direct lending. The core value addition from the implementation of AI/ML in ORM framework is for proactive risk identification and mitigation which consume valuable time of operational risk teams. 

Challenges 

Risk prediction and proactive mitigation is the need of an hour and its critical in view of operational risk wide and open scope. Risk Managers plans to spend more time on forward looking but always engaged in legacy framework implementation which consume their precious time and efforts, and they end up spending lesser time on proactive risk management which they always wish to.  

Below are few key challenges faced by Risk Managers during AIML framework implementation:

• Relevant risk data identification.
• Data segmentation.
• Risk data source relation and categorisation
• Transformation of raw data into meaningful risk information which is key to enable timely decision making and proactive response to risk exposure.  

Artificial Intelligence and Machine Learning (AI/ML) infusion in Operational risk

We are living in an information era where information is easily available but, effective and timely utilisation of the same is a challenge. This becomes more critical when we talk about Operational Risk Management. 

Operational Risk is inherent in all business activities including people, process, system, and external environment too. Idea is to take any data (segmented or non-segmented) and translate it into operational risk information using AIML algorithms such as Time Series Creation, Test of Stationarity, Seasonality and Trend, Exponential Smoothening, ARIMA, Regression, Model Validation: Error Measures, R2, Lift ROC etc. and convert it to meaningful OR information. 

Benefits

Calculated proactiveness is always better and adoption of innovation and technology will support organisations in establishing risk culture- in an automated way. With AIML infusion, organisations can have forward looking approach and enjoy following benefits:

• Proactive risk identification: Proactively identify and evaluate unstructured data about risky behaviours or activities in the organization's operations.
• Risk Reduction improved data processing: AI and machine learning techniques have made it possible to process structured and unstructured data in massive amounts. Datasets can even be combined to form new variables that unravel key relationships.
• Improved efficiency: Automation of repetitive tasks can help firms to reduce costs.
• Real-time and predictive insights: AI and machine learning tools can alert firms about new exposures faster than traditional tools, increase preventative risk advice, and help firms develop faster response times in critical situations.
• Improved decision making: Machine learning is associated with better decision-making through greater (predictive) insights and risk visibility.

"Risk is beyond coincidences"