AI-led Risk Management Transformation in Financial Services

The financial services industry remains a critical adopter of any new technological innovation, with the ever-present need to manage and mitigate risk effectively. With the recent emergence of AI, the banking, financial services, and insurance (BFSI) sectors have again been at the helm of innovation.

According to research, the global AI trust, risk, and security management market was valued at a whopping USD 1.7 billion in 2022. This number is expected to reach USD 7.4 billion by 2032, growing at a CAGR of 16.2%. [i] This remarkable growth highlights the transformative role that AI is playing in revolutionizing risk management strategies across industries, including BFSI. Using AI, financial institutions are enhancing their ability to predict and assess risks and strengthening their decision-making processes in real-time.

Foreseeing Risks with AI

AI powers financial risk predictions before they materialize, offering a massive strategic advantage for BFSI companies. Traditional risk assessment methods, frequently relying on historical statistics and static models, can fall short in an evolving market. With its superior analytical and predictive capabilities, AI can help BFSI companies accurately foresee market changes and trends to keep them ahead of the curve.

Machine learning (ML) algorithms – the foundation of modern AI engines- can analyze large datasets to predict emerging risk patterns and potential anomalies. AI systems, for instance, can accurately predict credit defaults by closely assessing a borrower’s transaction history, social media activity, and broader economic indicators in near real-time. According to McKinsey, companies can witness a 20-30% decrease in credit losses by leveraging ML models that can predict a customer’s tendency to default.[ii]

Implementing AI into risk forecasting models has become a mainstream use case for most BFSI players. A big multinational bank, for instance, utilizes AI to forecast marketplace developments and potential risks by analyzing financial information, market data, and buying and selling volumes. This proactive approach permits the bank to regulate its strategies and mitigate risks efficiently, illustrating how AI can minimize the chances of potential losses.[iii]

Automating Risk Evaluations

Risk automation is another crucial area where AI is helping BFSI organizations take significant strides toward the future. Traditional risk assessments regularly involved time-consuming manual procedures more prone to human errors. On the contrary, AI-driven automation is helping institutions streamline risk assessment processes, making them more efficient and accurate.

Robotic Process Automation (RPA), in conjunction with AI, can seamlessly automate repetitive tasks such as fact-checking and validation. This helps free up resource bandwidth for more strategic work. For instance, AI can quickly assess an organization’s financial statements, investigate compliance with regulatory requirements, and identify potential red flags. This simplifies and fastens the risk evaluation process and reduces the chance of oversight.

In the insurance sector, for example, AI completely transforms underwriting. An InsurTech startup recently achieved a radical feat by harnessing AI and ML to transform claims processing. The company’s proprietary claim resolution system settled a genuine insurance claim within two seconds, breaking all previous beliefs that such fast settlement times were virtually unattainable. The company’s innovative claim settlement process is led by an AI engine that can swiftly assess the claim, meticulously check the policy conditions, and execute numerous anti-fraud algorithms. After the claim is approved, the AI engine promptly sends payment instructions to the bank and gives the policyholder an instant notification of the claim settlement.[iv]

Real-time Insights

One key aspect that makes AI a game changer in risk assessment is its ability to deliver insights on a near real-time basis. This is a precious advantage in a sector where timely decision-making can differentiate between profit and loss.

AI systems can constantly analyze market conditions, monetary transactions, and operational activities to provide actionable insights. This allows BFSI companies to respond rapidly to emerging opportunities and risks. For example, AI-powered financial dashboards can help risk managers identify potential frauds, unusual trading patterns, and quick shifts in market conditions. This way, risk managers can stay prepared and take corrective action before a situation escalates.

For BFSI institutions, embracing AI for strategic and operational advantage is no longer a luxury but a necessity. As the industry evolves, those organizations that leverage AI effectively will gain a significant competitive edge. By staying ahead of the curve with AI-driven risk management solutions, institutions can safeguard their assets and unlock new opportunities for growth and innovation.