5 Ways the Financial Service Sector Can Benefit by Using AI for Risk Management 

 

For firms in the financial service sector, ignoring risks can prove to be fatal. Since the financial crisis of 2008, many FS firms have integrated advanced technological solutions to address key business risks. The use of Artificial Intelligence is being explored in various sectors. AI for risk management, in particular, has become very popular among FS firms.

Why Use AI in Financial Services and Risk Management?

Most firms in the financial services sector, from banks to insurance companies, collect vast amounts of customer data. Unfortunately, these large quantities of customer data are mostly unstructured. AI tools, with their advanced processing power and cognitive computing capabilities, can analyze/process enormous piles of unstructured data rapidly.

With faster data processing, analyzing the risk history of each customer, asset, or market situation becomes easier. Human operators in FS companies can use these insights to accordingly identify and forecast future risks.

Here are some practical cases where using AI for risk management is extremely advantageous:

  1. Create “What If” Scenarios

Financial service providers can create warning systems using AI tools to simulate ‘what if’ scenarios. For example, a lender can use AI-powered risk assessment tools to predict default rates while screening loan applicants –

  • Such lenders can easily avoid giving ‘bad loans’ by processing large numbers of customer data points.
  • Some data points that AI tools can instantly process include financial or behavioral activities of a loan applicant.
  • These data analyses happen in real-time so lender can make rapid, on-the-spot decisions regarding loan applications.

AI-powered data analytics tools can pick up the slightest signs of risk that typically go unnoticed by human operators.

  1. Manage Financial Threat Signals
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Screening customer transactions is a risk management area that can benefit immensely from AI-powered solutions. The real-time tracking of fraudulent activities and external risk factors helps FS companies address key threat signals.

  1. Help Security Leaders

AI-powered risk management tools can help security leaders of financial service companies make better, data-driven decisions. Be it responding to security incidents or creating business continuity plans – equipping human operators with real-time, AI-powered data always leads to better security outcomes.

  1. Create Up-to-Date Risk Environments

Financial service companies can use AI tools to identify key business risks that could have adverse impacts on the organization’s commercial strategy/operations. With AI-powered tools, they can continuously monitor their internal and external operating environments.

These tools can also identify changes to external regulatory environments. Based on these assessments, human operators can identify the most immediate internal or external risks their companies face. More importantly, these insights are available in real-time.

Managers can constantly integrate these insights into their risk management frameworks, keeping them relevant and up-to-date.

  1. Build Robust Threat Intelligence Databases

Many financial service providers are still using traditional, signature-based threat recognition tools. Unlike these outdated tools, AI-powered threat detection tools can detect and deal with zero-day malware. In computing, zero-day malware refers to malware or viruses with unknown capabilities.

Security patches to deal with such malware are non-existent. Unless security leaders create appropriate mitigation strategies, hackers can keep exploiting their targets using these seemingly undefeatable malware attacks. Here is how AI tools can help organizations address zero-day malware threats:

  • Simulate End-to-End Cyber Attacks: Advanced AI tools can conduct vulnerability scanning to detect new vulnerabilities in the firm’s security system. AI tools do not use rule-based methods or traditional statistical correlation patterns. These tools perform dynamic malware analysis to determine the malicious nature of each file by going through their respective code-bases.
  • Behavioral Detection: AI tools can determine if a particular software or program displays any malware-related characteristic by assessing how it networks with the system. Unlike traditional, rule-based statistical techniques, AI-powered behavioral detection tools can unearth deep, hidden patterns in potentially suspicious programs or software.
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As these AI for risk management tools evolve, they become more contextually aware. They enable firms to perform faster data analysis, observe patterns of threats/risks in real-time, and create prescriptive risk-management solutions.

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