How Predictive Analytics Is Transforming The InsureTech Industry

Businesses and industries today are sitting on humungous amounts of data. Legacy data going back several years are virtual goldmines of information. They map the entire trajectory of functioning of businesses, from marketing focus and trends to operational changes and sales curves. One use of this legacy data is in mapping patterns of behavior – of people interacting with the business over years. Such mapping gives valuable operational insights, showcases strategies that caused a shift in thinking, marks success of campaigns, and the overall progress of the business. A significant insight of legacy data; that is bringing a paradigm shift in the way industries function today is the technology of Predictive Analytics.

In its simplest form, Predictive Analytics can be called the act of foreseeing the future, identifying quality failures, phenomenal successes and taking corrective action, or placing checks and balances to optimize future decision making. Predictive analytics however doesn’t rely on mere conjecture, but actual analysis and learning of past data to forecast inventory, manage resources, attract, retain and grow a likely profitable customer database.

Organizations across industries are turning towards predictive analytics to solve some of their most pressing problems, uncover new opportunities and give them new insights. Some common applications of these analytics when integrated within applications and systems include;

• Predicting buying behavior – The retail industry is a prime example of using a person’s purchasing history to learn about their customers. Walmart, a leading retailer incorporates predictive analytics in PoS to analyze the customer’s buying patterns, making the shopping experience personalized and enjoyable.
• Content recommendations – This is one of the application’s most recognizable and relatable tool, where companies tailor content depending on their customer’s past history. OTT channel Netflix, differentiates content for its consumers based on keywords, ratings, genre, using highly advanced analytics.
• Fraud detection – With digital content being consumed phenomenally, cybersecurity is becoming an omnipresent threat. The predictive analytics model identifies anomalies in the system, and can detect unusual behavior like cyber hacks to determine threats based on past user patterns, very critical in fraud detection.

Detecting fraud and preventing it is one of the key attributes of leveraging predictive analytics, in the Insuretech industry. A modern day analytics solution like Charlee.ai performs holistic and deeply researched analysis of several data points to assist Claims Management make informed decisions. An insured customer’s data for example, can extract behavioral information that correlates with high-risk for fraudulent activities. Insurance companies can identify red flag fraud indicators and suspicious behavior patterns with subsequent real-time alerts sent to the assigned Claims Examiner. Once the red flags are identified and analyzed, the Examiner can consider referring the claim to their SIU for further investigation. Risk scores can be set against customers to ease the claims process, including maneuvering carefully around litigation and other critical matters that can arise any time.

In insurance, predictive analysis is the forensic tool that points towards fraud recognition and prevention. As a result, insurance carriers can implement transparency and accountability in internal employees, while being aware of and quickly respond to potential suspected fraud.