Using AI and ML driven RPA to identify fraud in insurance claims
Machine learning is a subset of Artificial intelligence (AI) that incredibly provides systems the ability to learn and improve without being explicitly programmed. ML along with RPA has revamped the insurance industry and is effectively beneficial in fraudulent claim detection and prevention to facilitate the early detection of suspicious claims. Hence, it is one of the game-changing innovations that efficaciously saves time, money, and effort.
Problems encountered by insurance companies with traditional methods.
Insurance companies suffer a loss of approximately US$30 billion a year in fraudulent claims. Conventional methods fail to identify, combat and curb frauds because they are inflexible, lack core options like Cross-integration, data analyzing, real-time behavioural profiling, also involve more human intervention. It is unable to review financial transactions effectively and takes a lot of time in evaluating the uneven patterns, and in most cases, it misses out on the subtle yet critical details. It could result in massive financial losses.
How ML driven RPA helps in fraud detection?
AI and machine learning tools help to spot unusual patterns that are unnoticeable by the human eyes. For instance, reviewing any insurance claims, it compares the new claims to the existing data and helps to detect fraudulent and non-fraudulent claims. It continuously monitors customer behaviour to understand their patterns. If a customer claims for insurance, first it detects the pattern. In case of unusual patterns, it raises the red flag by indicating odd claims and highlighting for further investigation. Hence, without human intervention, it will analyze the claims and help in reducing large-scale frauds. The continuous remodelling of this type of scheme and requisition of variations in data analysis will enable to anticipate the detection of new fraud schemes.
It significantly compares the existing data with the new data to generate deep-dive insights. It works in three stages-
Underwriting stage- In this stage, it identifies the abnormal behaviour patterns with the linkages to frauds.
Validation stage- With high levels of precision and accuracy, ML advanced analytics help insurers validate larger cases in a short period.
Investigation stage- Analytics compares the validated claims by cross-referencing the claimant’s profile through various sources and identifying the linkages between the policyholders and fraudulent activities.
Leading Technologies in Machine Learning & Rapid Process Automation
Machine learning blended with RPA can be helpful to minimize the fraudulent claim cost. One of the studies has shown that Automation can reduce the cost of the claims journey by as much as 30%. (Source). The Top technologies in Machine learning and Rapid Process Automation are as follows-
1. Predictive analytics- It is a sophisticated and supervised technique that detects fraud that has already happened and prevents it from transpiring.
2. Rapidminer- It leverages machine learning to discover unusual behaviours, fraud patterns, and Anomalies.
3. Logistic Regression- Logistic Regression is an appropriate projection algorithm that widely helps anticipate credit card fraud detection and credit scoring.
4. Artificial intelligence- AI-powered insurance virtual assistant or bot can review the claim and send it to the fraud detection algorithm before transmitting instructions to the bank for the payment of claim settlement.
5. The combined power of predictive analytics NLP and AI- It helps the customers to provide sophisticated and personalized services with customs sales tactics. Based on the customer preferences, exact insurance products or services are enumerated in the marketing activities.
6. Image recognition technology- When it comes to damage analysis cost estimation and claim settlement, the bots will carry out the process by scanning the picture and videos.
How Kanerika helps clients with Machine learning driven RPA solutions?
Kanerika’s AI driven integrated solutions have an advanced predictive model which has profound accuracy in identifying the potential fraudulent claims and early prevention to drive down the unnecessary cost. It uses deep anomaly detection to anticipate fraud detection quickly, reduce the loss ratios, acquiesce straight-through processing. Kanerika offers Machine learning driven RPA solutions that help companies in various verticals by swiftly incorporating controlled, digitally enhanced automated platforms that maximize productivity by automating the executive-level tasks, enrich service quality, excellent customer experience, and help in rejecting spurious claims.
How does Kanerika deploy ML & RPA tools?
Kanerika deploys ML’s advanced analytics in which your RPA engine can learn, predict, and act responsively. It includes underwriting, marketing, customer service, claims, fraud detection, and prevention. We streamline end-to-end processes and automated solutions from data scanning and processing to verifying policy details and determining gaps or errors. By evaluating large sets of genuine claims, we deploy ML driven RPA software that creates models of customary insurance claims. When any deviation from a typical claim raises the flag as potential fraud. It enables insurance companies to reduce risks, frauds and achieve exceptional results.
Kanerika offers top-notch ML driven RPA solutions that help in fraud detection, customer service, risk assessment & mitigation, fraud prevention, substantial operational excellence, suspicious behaviour detection, and regulatory compliance. Kanerika deploys long-term intelligent automation strategy and AI fraud detection models that will bring exceptional results.
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Kanerika is a niche consulting firm building efficient enterprises with deployment of automated, integrated and responsive solutions. Kanerika enables efficient enterprises through its unique digital consulting frameworks and AI enabled composable solution architecture. Kanerika assists a number of the leading brandsworldwide in increasing their speed to respond in evolving market conditions, reducing their cost of operations andempowering them with the appropriate tools and insights for effective decision making. Kanerika was started in 2015 by a few industry veterans with the objective of helping clients build efficient enterprises. For more information visit https://kanerika.com/