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AI for Risk Management: A Game Changer for The Insurance Industry

ByBRIDGE i2i

Sep 23, 2021
AI for Risk Management

AI is being used to reinvent countless traditional business functions and processes. AI-powered tools can give companies nonstop monitoring capabilities. While gaining these data-based insights, companies can anticipate risks much faster. Can insurance companies use AI for risk management to create an industry culture of transparency and accountability? Yes. Reducing the risk of corruption and fraud is easier with AI tools. Embedding AI into the risk management process also promises other benefits for insurance companies.

The Need to Use Data Analytics and AI technologies in the Risk Management Process

Traditional risk management processes have always been based on perceptions and subjectivity. In the emerging risk landscape, these approaches won’t be effective anymore. According to the NAIC (the National Association of Insurance Commissioners), the insurance business is laden with risks like –

• Underwriting risks
• Credit risks
• Employee safety
• Market risks
• Changing federal regulatory landscape
• Operational risks

Addressing this wide variety of concerns is only possible if insurance companies can anticipate risks before they materialize. Leading companies in AI can help insurance companies treat risk as an evolving concept, not a static one.
That means identifying emerging risk trends and metrics by leveraging unprecedented velocities, volumes, and varieties of data. That can only be done by AI-powered data optimization tools.
Top insurance companies and risk management organizations already understand the importance of AI for risk management.

Achieving Strategic Goals: In 2020, Gartner surveyed thousands of leading risk managers in the world. 90% of the respondents said insurance companies would struggle to achieve strategic goals unless they use advanced risk management techniques.
Fraud Detection: The recent SolarWinds attack gave hackers access to thousands of terabytes of private and government data. Many insurance companies were also victims of this coordinated cyberattack. It proved that the infrastructure of monitoring and authenticating digital customers is still lagging in the insurance industry.
Employee Risks: According to a 2021 survey from Gartner, 30 to 40% of employees are likely to continue remote working in the post-COVID-19 world. Shifts in employee management processes will drive more uncertainty in the insurance industry. For many insurance companies, this shift requires a comprehensive overhaul of most workplace and security policies.
Revenue Risks: According to a recent report, insurance sales will not return to pre-pandemic volumes until after 2022.

These are the reasons why many insurance companies have – to varying degrees – reinvented their risk management functions. But, there’s room for improvement. AI tools can enable risk sensing, modeling, and tracking. They can also take over time-consuming risk management activities.

Here’s a rundown of some practical use cases of integrating AI-driven analytics and risk management for insurance companies.

Risk-Free Claims Management
Settling claims is a convoluted process in the insurance industry. It entails processing large volumes of varying data. When data is being passed through various stakeholders – agents, underwriters, brokers, claimants, etc., there’s always the risk of errors. AI tools and programs can automate the claims management process.
Insurance companies can automate data checks, interactions and streamline all end-to-end processes. Tokio Marine, a Japanese insurance company, has already implemented AI-powered claims management tools. Their fast-tracked, AI-powered claims management tools reduced human error by over 80% in less than a year.

Risk Identification and Prioritization
Insurance companies are exposed to risks from two sources –

• Internal – Human errors, lack of advanced data-processing tools, overexposed business portfolios, etc.
• External – Government regulations, macro-economic changes, social changes, etc.

As insurance companies become bigger and more complicated, they’re exposed to more of these internal and external risks. They’re also exposed to larger volumes and varieties of ‘big data.’ AI tools can help insurance companies integrate internal and external risk assessment strategies.

These tools can leverage all data points to identify emerging risks. More importantly, these tools can extend an insurance company’s understanding of the current risk landscape. Why waste data that is readily available? AI-powered data optimizers can generate insights from this data to –

• Detect all potential risks (continual monitoring of all data)
• Assess the financial and other associated impacts of these risks.
• Create an analytical framework for rating the financial and strategic impacts of different types of risks.
Once insurance companies become aware of their most pertinent risks, they can create timely risk management or mitigation strategies. Insurance company leaders can implement these strategies down to the operational level. Technically, every worker in the insurance industry can benefit from the insights generated by AI-powered analytical models.

Fraud Detection
Insurance fraud costs American consumers over $40 billion per year. Thankfully, AI tools can drastically improve an insurance company’s fraud detection capabilities.

• AI-driven risk modeling techniques can simulate “what if” scenarios to decide the most optimal response strategies. These scenarios can be based on emerging fraud trends in the insurance industry or past risk occurrences.
• Insurance companies can also ask top AI companies to create AI-powered data analytics systems. These systems can be used to assess the effectiveness of all risk-mitigation plans deployed by the insurance company.
• Deep anomaly detection is a popular AI-powered technique. It processes all customer data points – from transactional to geospatial. Deep anomaly detection tools can spot patterns and identify scams (that may seem unrelated to human data analysts). These tools can save insurance companies a lot of money.
• AI tools can also be used to create more accurate credit risk prediction tools. Constantly evolving machine learning algorithms are likelier to predict defaulters with higher degrees of accuracy than conventional methods and tools.

Insurance companies that leverage AI for risk management are better equipped to deal with the upcoming financial, regulatory, and industrial. That’s why AI is a game-changer in the insurance industry. AI adoption will determine which insurance companies survive in this increasingly competitive industry.

BRIDGE i2i
Author: BRIDGE i2i

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