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Intelligent Risk Assessment in Insurance

Risk Management is a core function within the insurance industry. It is a vital responsibility of the underwriting team.

Insurance companies collect data scattered across different business units in various formats – some of which are paper and digital,  most of which are typically unstructured. The underwriting team doesn't have immediate access to the information required for internal and external decision-making, resulting in delays in making decisions and costly mistakes.

Underwriters spend a lot of time searching for information necessary to complete their work. Due to the sheer volume and complexity of unstructured data, manual analysis is costly, time-consuming, and tedious. This data and information disconnect makes maintaining compliance, avoiding fines, and preserving brand reputation even more difficult for insurance companies in an ever-changing regulatory environment.  

Manual processes can't scale alongside the scope and speed of business growth in any industry. Insurers are digitalizing data and historical records, resulting in increased knowledge and savvier knowledge management.

By implementing artificial intelligence (AI) and machine learning (ML) across their operations, they are creating compelling experiences for their team that are personalized to their client's risk profile.

Insurance Industry using AI and ML: Smarter Risk Assessment

Any industry's main goal and objective is to minimize risk as much as possible. In Insurance, the underwriters and underwriting teams are primarily involved in the risk management activities.

Underwriters must deal with many proposals containing data in various shapes, sizes, and volume. It becomes difficult to thoroughly check each proposal before issuing the policy.

Enter the role of AI and ML to classify these proposals as High Risk, Medium Risk, and Low Risk, allowing the underwriters to save valuable time by targeting the ones with the highest risk.

Text analytics platforms use Natural Language Processing (NLP) to help improve worker productivity. NLP reveals hidden insights in health profiles and findings of medical practitioners (comments on X-rays, ECGs, TMTs, etc.) that are digitalized and converted into meaningful data. This information is further utilized on ML for AI-based assessment of risks associated with the life to be underwritten.

These gains in efficiency allow underwriters to process complex profiles and proposals faster, making them more responsive to the prospect's queries and apprehensions, resulting in better customer experience and higher customer satisfaction.

Below are two use cases that utilize AI and ML for risk management in the Insurance industry.

#1 PREDICTIVE MODELING AT PROPOSAL SUBMISSION

Insurance companies receive massive amounts of proposals on a regular basis. The key is to sift through these proposals and quickly identify those with the probability of "good" behavior (i.e., absence of claims, good persistency, absence of fraud, lapse, and surrender) post-issuance.

AI can increase efficiency and automate workflows by accelerating underwriting processes, delegating only high-risk proposals to human attention, offering better data-informed insurance policies faster, and improving customer experiences.

With AI, underwriters can pinpoint optimal rates based on individual customer profiles with minimal manual effort.

AI-based pricing models also help reduce the time it takes to introduce new pricing frameworks across the underwriting lifecycle.

Underwriters can also use AI solutions to get information and insights faster. For example, insurance companies can deploy chatbots externally and internally to help knowledge workers quickly deliver relevant insights through remote digital experiences during the risk assessment and proposal.

Predictions from ML and AI models are typically a score that is indicative of the likelihood of the event that is of interest. Predictive models may use the following variables obtained from the proposer's profile:

  • Age
  • Education
  • Occupation
  • Gender
  • Location
  • Credit history
  • Credit score
  • Policy-related information such as premium, payment term, payment frequency, benefit term, channel
  • Health profile of life assured as informed in the proposal form
  • Medical examination records
  • Income

Once a model is built to predict proposal behavior in the future, proposals are categorized into different risk buckets ranging from high to low based on each segment's expected good behavior.

The financial underwriting team uses this information to understand how it will behave in the future based on the score and also to avoid the risks involved.

The significant predictors in this use case typically include factors like :

  • Premium amount
  • Customer demographics (location, state, occupation, education, gender)
  • Period for which the customer will pay the premium
  • Payment frequency (monthly, quarterly, half-yearly, etc.)
  • Agent and channel behavior
  • Period for which customer will get policy benefit
  • Sum assured (fixed amount, pre-determined by the insurer, which is paid in case of death during the tenure of the policy)
  • Product behavior in the past
  • Customer behavior with their existing policies
  • Is the customer a high net worth individual?
  • Annual income of the policyholder
  • Customer vintage – how long the customer has been associated with the insurer
  • Agent Vintage – how long the agent has been associated with the insurer
#2 PREDICTING RISK OF EARLY CLAIMS AND FRAUD

Fraud identification and detection is essential for the Underwriting and Claims teams. As the number of claims increases, it becomes difficult to investigate whether it's a genuine or fraudulent claim. Ideally, we would like to identify the likelihood of any claims being fraudulent or repudiated at the time of intimation. Insurance companies can save a vast amount of sum assured by targeting high-risk claims.

Anticipating the claim is a crucial component of the underwriting function of any insurance company. Life insurance product design assumes a certain number of claims within the first year of the policy.

ML is the latest tool at the disposal of the underwriter that uses past claims experience of the organization and flags proposals that are high risk for early claims.

ML models compare the profile of the proposer and proposed life assured with the existing profiles of the insurer based on a segmented approach like district, state, and demographics to generate recommendations. Based on ML recommendations and organizational guidelines, the underwriting team decides to underwrite the proposal, perform further investigation (field / financial or medical), or defer/decline the proposal.

Insurance companies spend much of their time and resources investigating claims received within three years of policy issuance, especially if the claim is within one year.

Data Science and ML Algorithms Come to the Rescue of the Life Insurers

ML algorithms will learn and identify the pattern demonstrated by fraudulent claims, e.g., generally arising within one year. Some distribution partners have higher vulnerability, appear regionally, and demonstrate consistent socio-economic demographics.

ML models show the probability of a fraudulent claim.

The claim processing team segregates claims into three buckets based on the threshold for each bucket based on past trends. The insurer is allowed to focus their investigation effort on high-risk claims and settle low-risk claims quickly.

Based on the availability of resources, insurers apply different strategies to handle each risk bucket. These range from field-level investigation of policies in the highest risk buckets to in-house verification or call-center engagement for moderate risk buckets.

The significant predictors in this use case typically include factors like :

  • Age of Insured Customer
  • Customer demographics (Income, state, occupation, education, gender)
  • Relation of customer with nominee
  • Payment frequency (monthly, quarterly, half-yearly, etc.)
  • Sum assured (fixed amount, pre-determined by the insurer, which is paid in case of death during the tenure of the policy)
  • Premium amount
  • Period for which the customer will pay the premium
  • Period for which customer will get policy benefit
  • Claim's ratio for the agents and channel
  • Claim's ratio for the location (district or state)

Early claims models described above may be further subdivided by product, channel, etc., to achieve the desired accuracy on each portfolio.

The above use cases show how ML, AI, and Data Science play a crucial role in Risk Management in the insurance industry. Click here to learn more about the industry implementation.

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Yogesh Dhavale
Yogesh Dhavale
Yogesh is a part of the data science team at Aureus. He holds a Bachelor’s Degree in Engineering and Technology from Veermata Jijabai Technological Institute (VJTI), Mumbai.

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