Insurance executives need accurate loss predictions so that they can set reserves appropriately. Machine learning delivers the high-quality predictions insurers need for smart decisions. 
“Actuaries and statisticians have used historical data to recognize patterns in claims and predict future losses for over 100 years. They’ve been pretty creative in doing so, using tools in line with the technology of their time from minimum bias all the way up to decision trees. The level of sophistication and tools has changed over time, and I look at Machine Learning and AI as transformative for the way we try to solve the same problems while also gaining insights from places where traditional methods fail,” stated George Argesanu, Global Head of Pricing and Portfolio Management for Personal Auto and Property, AIG.
As for an example, let’s look at Allianz. Allianz Global Corporate & Specialty SE (AGCS), the corporate insurance carrier of Allianz SE, is working with Praedicat, an InsurTech analytics company based in Los Angeles, to better predict the key catastrophe liability risks of the future. By combining Praedicat’s predictive modeling approach with AGCS’ underwriting processes and extensive liability risk portfolio analysis, the companies aim to identify the next generation of catastrophe liability risks for business customers far earlier than under current methods. Praedicat’s modeling engine uses machine learning technology to scan large volumes of data from peer-reviewed science publications and profile the likelihood that products or substances will generate litigation risks over their lifecycle.
By complementing the traditional experience-based underwriting and portfolio management of liability risks with predictive analytics, AGCS and Praedicat aim to combine the best of both approaches in this new risk assessment methodology. Using forward-looking data models in addition to historic loss data analysis and risk engineering assessments, AGCS liability underwriters globally will be able to better identify and assess future liability risks for industries or single companies. Asbestos, which caused insured losses of $71 billion globally until 2011, is one high-profile example of such a man-made liability disaster. Read more at Allianz.
Dynamic, responsive price optimization
With changing consumer needs and available technologies, insurers are bound to adapt pricing models to lifestyles. For that, they use machine learning to build optimal pricing models to make more optimized pricing decisions.
MetLife Auto & Home is expanding its usage-based auto insurance program, My Journey, with a new smartphone app to monitor and improve its customers’ driving. Powered by technology from tech firm TrueMotion (Boston-based tech company that combines the power of mobile technology, machine learning and data science to impact the rising rates of automobile crashes and fatalities), the app utilizes the capabilities of an iOS or Android smartphone to provide drivers with quick feedback to both improve their driving and lower their auto insurance rates.
The My Journey program app automatically tracks key driving behaviors, including total miles driven, time of day, road type & conditions, hard braking & harsh acceleration, and phone-based distracted driving, in order to arrive at a score for each trip. The app does so by leveraging the sensors that are built into smartphones to continuously analyze data as the car is in motion. The app calculates and immediately displays an overall score for each trip from 1-100, with 100 being the safest possible trip. A cumulative safety score is built as time goes by. Read more at TrueMotion.
“Technology, as the big enabler of the revolution we are living now, moves everything from a static framework such as historical data is used to predict future losses, to a really dynamic environment where the lines between past and present are blurred. The self-learning element of AI is the driver of this dynamic environment that creates a continuous loop of feedback and decision-making,” Argesanu shared in the report called “Anything You Can Do, AI Can Do Better: Insurance Applications for Machine Learning.”
Shift Technology, a startup based in France, helped a European coalition of insurers analyze 13 million claims. The technology identified 3,000 new cases of potential fraud, including a large, organized crime scheme that impacted nearly all the coalition’s members. The scam had siphoned millions of Euros from the group’s insurance company members over the span of many years, according to a Shift Technology case study. Read more at MetLife.
Transamerica, a holding company for US-focused life insurance companies and investment firms, is a subsidiary of Dutch life insurance multinational Aegon. Transamerica provides life and supplemental health insurance, investment, and retirement services to 27 million customers. Transamerica uses EMAP to create a comprehensive view of its clients in order to best serve their needs given an array of financial services the company offers.
The company integrates data from across its insurance, retirement and investment lines of business with third-party data. Transamerica’s Enterprise Marketing and Analytics Platform (EMAP) pulls in data from more than 40 sources, including consumer income and social media data. All that information is used to identify new patterns.
Transamerica decided to tackle its data analytics challenges with a Hadoop-based data lake. The company uses Cloudera’s distributed Enterprise Data Hub for storing structured, semi-structured and unstructured data. Informatica’s Big Data Management (BDM) product handles the vital data management functions, including data ingestion and integration, data profiling, and data quality.
Transamerica uses different processing engines, such as MapReduce, to parcel out work to various nodes and organize the results. The company also deployed Spark, a fast, in-memory data processing engine that’s particularly efficient with SQL and machine learning. Transamerica relies significantly on machine learning to draw insight from its data. Machine learning automates data analysis through algorithms that iteratively learn to uncover insights they weren’t specifically programmed to find. The company uses H2O, an open-source machine learning platform. Using H2O, Transamerica leverages in-memory distributed processing on Hadoop and lets data scientists run large numbers of machine learning models using common programming languages for big data, such as R, Python, and Scala. Read the full article at Information Management.
QBE Insurance Group (QBE) not only closed an investment into Cytora through its venture arm in 2017 but also entered an agreement to use the three-year-old London-based startups’ technology. Cytora uses artificial intelligence (AI) and open source data to help commercial insurers lower loss ratios, grow premiums, and improve expense ratios.
In 2018, the Cytora Risk Engine will be deployed across QBE property and casualty lines. The Cytora Risk Engine, driven by machine learning algorithms, combines an insurer’s internal data on a specific cover with external information from a broad spectrum of sources. This generates a risk score, which provides enhanced insight into expected claims activity on the whole portfolio and also at an individual risk level. Read more at QBE.
Another company that uses Cytora’s technology is the property and casualty insurance and reinsurance provider XL Catlin. XL Catlin said it will use Cytora’s expertise in sourcing and analyzing data from multiple sources and combining them to create new insights into risk. Cytora’s Risk Engine captures the online footprint of risks clients are continuously facing by crawling data from company websites, news articles, and government datasets, and processes it using AI algorithms in order to predict future claims, attractive risk profiles, and quality of risks. Read more at Cytora.
Security & privacy
Aetna has launched a new security system for its consumer mobile and web apps that, in something of a twist, makes passwords optional. Instead of a password or fingerprint being the only barrier to entry, Aetna’s new behavior-based security system monitors user devices and how and where a consumer uses that machine. Consumers can add biometric protection available on their devices.
That risk engine takes in data from many attributes of the device (software configuration, operating system version, etc.), in addition to benign attributes of consumer behavior (for example, how a mobile device is held when texting and location of the device), and matches these attributes against a device signature and a model based on previous behavior.
The risk engine binds a consumer to one or more of the devices they typically use. If they use a new device, the authentication request may include a PIN or biometric to confirm the consumer wishes to bind their identity to a new device. The risk engine compares the benign behavioral attributes to the existing behavioral model and determines a risk score based on the match.
“The risk engine is using unsupervised machine learning to match attributes to the existing model, so the more data provided into a model the better it performs over time,” Jim Routh, Chief Security Officer at Aetna, explained. “Therefore, the more often the consumer uses the application, the more effectively the risk engine performs. Aetna provides consumers with choices on how they wish to interact and which types of biometric controls they prefer on their devices. Giving consumers choices gives them more convenience while also providing them with better security to protect their information.” Read more at Healthcare IT News. 
There are many more examples of how machine learning is being deployed by insurers across business functions. Over time, we will see an increasing number of insurers finding ways to use automation & ML/AI to bring efficiency to operations and deliver value to millions of their clients. Machine learning and inevitably artificial intelligence are changing the insurance business, giving an edge to large companies and endless opportunities for technology enthusiasts to impact one of the most complex industries in every nation.
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Elena is a research professional with a background in social sciences and extensive experience in consumer behavior studies and marketing analytics. She is passionate about technologies enabling financial inclusion for underprivileged and vulnerable groups of the population around the world.
- ^ January 11, 2018 (letstalkpayments.com)
- ^ Machine learning delivers the high-quality predictions (www.ft.com)
- ^ stated (1.fc-bi.com)
- ^ George Argesanu (www.linkedin.com)
- ^ Read more (www.agcs.allianz.com)
- ^ Read more (gotruemotion.com)
- ^ 3,000 new cases of potential fraud (www.shift-technology.com)
- ^ case study (www.shift-technology.com)
- ^ Read more (blog.metlife.com)
- ^ Read the full article (www.information-management.com)
- ^ Read more (qbeeurope.com)
- ^ Read more (cytora.com)
- ^ Jim Routh (www.linkedin.com)
- ^ Read more (www.healthcareitnews.com)
- ^ Machine learning and inevitably artificial intelligence are changing the insurance business (medium.com)
- ^ Elena Mesropyan (letstalkpayments.com)
- ^ see all (letstalkpayments.com)