Step 2: Assign every entity to its closest medoid (using the distance matrix we have calculated). If so, make this observation the new medoid. Model Validation • "Model risk is the potential for adverse consequences from decisions based on incorrect or misused model outputs and reports. "  • "Model validation is the set of processes and activities intended to verify that models are performing as expected, in line with their design objectives and business uses.
Until even 18 months ago, the proportion of customers that used digital channels to buy insurance from Max Life Insurance Co. Ltd was less than 5%. When the agent meets the prospective client, the conversation can start from retirement plans, since that is what the client looked up online and for which the agent "already has the context". Nangia acknowledges the role of digital competitors such as PolicyBazaar.com in "doing a good job as aggregators," but adds that there is ample scope in the market for companies with different business models and customer segments for tapping into the insurance sector. The younger generation, especially millennials, are agreeing to share a lot of their data online, which is opening up newer possibilities in insurance, according to Nangia.
Ant Financial Services Group, a subsidiary of e-commerce giant Alibaba, last month introduced an automated system to assess car damage by scanning accident-scene images and calculating payouts on insurance claims. If there it's consolation to human, most pilot projects to date are tapping the capability of machines largely for assistant roles involving repetitive, high-volume and rule-bound tasks. Andy Gillard, Asia Pacific digital operation leader at EY, one of the world's "big four" accounting firms, has looked at the wide-ranging applications of Robotic Process Automation across the securities, banking and insurance sectors. Ant Financial's car damage assessment system builds upon the second level of artificial intelligence called "machine learning."
And some insurance pioneers are already taking AI to the customer frontline, using it to streamline claims, answer basic customer queries and, increasingly, to offer straightforward advice about complex products to customers in a codified and consistent manner. Whether deployed alone or to augment agents and employees, AI offers insurers the potential of significant efficiency gains and scalable ways to improve service. Such assistants will, in time, evolve to answer more difficult questions and support the sale of more complex insurance products. Spixii is in early testing for both P&C and life insurance sales.
Themes ranged from providing more personalised products and services, better risk management, offering customers greater insights into their transaction, personalised money management systems and real-time AI chatbots. Axyon AI from Italy: offers Deep Learning-powered Artificial Intelligence solutions for finance businesses like hedge funds. Sentimer from Spain: Sentimer Technologies is an Artificial Intelligence chatbot platform for customer acquisition, cross-selling and service for banking, insurance companies and financial services providers. Spin Analytics from UK: Spin Analytics brings digital transformation in Credit Risk Management by leveraging predictive analytics, AI and ML techniques on Big Data.
In the United States, for example, insurance fraud--excluding health insurance fraud--incurs an estimated $40 billion in costs every year, boosting premiums across the board. As companies struggle to cut costs by mitigating the effects of fraud, predictive analytics algorithms scrutinize claims in a multistage process designed to help insurance companies efficiently detect and eliminate fraudulent activity by revealing insights into fraudulent patterns and claims data. By implementing IBM SPSS predictive analytics solutions, the Infinity Property and Casualty Corporation of Birmingham, Alabama, gained the ability to closely scrutinize claims histories, flagging suspicious claims for further investigation while fast-tracking legitimate claims. To learn more, discover the full scope of IBM SPSS predictive analytics capabilities.
More than half of today's insurance companies use machine learning for predictive analytics, according to a new report by Earnix, an analytics software provider for the financial services industry. Roughly 200 insurers were surveyed as part of Earnix's global "Machine Learning: Growing, Promising, Challenging" study, and they were prompted to select all business areas applicable to them. In total, 70% deployed the technology for risk modeling, the study found. Industry consensus is machine learning will bring significant change to insurance over the next five years, with 71% of companies believing investments in the technology will increase, Earnix says.
A majority of enterprises in financial services are undergoing a full-cycle digital transformation, with AI-led projects in the spotlight. More than 75% of banking and insurance companies are undergoing full-cycle transformation, while 20% are transforming partially, according to the report. The respondents that said their organizations are transforming, indicated that five AI-supported activities play a significant role in that process: machine learning, cognitive AI-led processes, automated predictive analytics, institutionalization of enterprise via AI, and robotic automation. Beyond the front office, AI has plenty to offer to the middle and back offices as well.
That's what's happening with artificial intelligence and big data; as the barriers to implementation disappear (cost, computing power, etc. We're all carrying the equivalent of Star Trek's tricorder around in our pockets (or an early version, at any rate) and smartphones and other smart devices will continue to advance and integrate with AI and big data to allow individuals to self-diagnose. In the very near future, AI financial advisors will begin to replace human advisors. Building on the idea of the AI guaranteeing your personal loan, your insurance company will also be transformed by artificial intelligence and big data.