February 7, 2018
Machine learning and artificial intelligence will become the most defining technologies in banking and beyond, which led some of the most powerful institutions to seek partnerships, investments, and in-house developments to take advantage of application potential of machine learning and AI.
Let’s look at a collection of examples of how leading institutions are utilizing machine learning to unlock value from the vast data pools they command and continuously accumulate.
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 the full story on the way Aetna replaces security passwords with machine learning tools.
We are starting to deploy various aspects of AI into production. Machine learning, deep learning, natural language processing and image recognition will each play an important and growing role in our business, Simeon Preston, AIA Group COO, said.
Preston shared that AIA has deployed machine learning to optimize its actuarial modeling, embedding chatbots into its service proposition in several markets, and uses an AI engine to improve insurance claims outcomes in Australia. Read more.
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 ca ...