AI Use Cases 

Create more personalised insurance pricing based on actual monitored customer behaviour. Examples would include 'black box' functionality in the car or wearable sensors on the body . Essentially the customer agrees to swap hitherto private data in return for what they expect to be lower pricing as the insurer can better measure real risk - and potentially the exact circumstances around an insurance claim.

Recommend lifestyle improvements to consumer to support insurance policy coverage (life, health, property) through “insurance­ robo-­advisor" (startup Clark). There will be cultural contexts in which this may work better than others.

Assess asset valuations (e.g. mark-to-market, regulatory capital assessment etc) for both internal risk and market control but also to ensure compliance with regulatory testing regime.

Use market and external indicators to predict risk of corporate distress and potential bankruptcy. Data sources will vary but may include personal data trails left by key directors.

Automate (robo-advisor) recommendations for users' investment portfolio advice based on user-completed survey (users must purchase/manage portfolio themselves)

Automate property manager role with chatbot acting as concierge and interacting with tenants. This is still at an early stage.

Lower the loss ratio for insurance companies through portfolio management. Note that the traditional insurance approach to portfolio management is under increased risk from data-heavy machine learning which will deliver increasingly accurate individualised risk pricing.

Analysing payment transactions to flag potential fraudulent activity - which is typically automatically blocked and usually requires human intervention to then unblock.

Identify and sell new mass market insurance consumer products - these may use the advanced data processing and risk pricing capabilities of AI to deliver low-cost niche products, e..g drone insurance, cracked mobile phone screens or specific use case products

Match patients with the right doctor for insurance purposes based on patient, supplier and insurance information

Enhance life insurance by improving life expectancy prediction and underwriting risk by analysing selfies (startup Lapetus). Selfies will provide key indicators - potential alcoholic intake for example. This will raise privacy concerns in markets like Europe,

Automate aggregation of personalised property listings for real estate customers

Invoice redirection fraud takes place when criminals contact a business claiming to be from one of their suppliers, saying that they have changed bank and requesting that an invoice is paid into a different account.

Automated investment decisions by funds that are allocated capital. Funds include Voleon, Castle Ridge Asset Management and Man AHL.

Evaluate investment opportunities in early stage companies by crunching data on comparables, market factors and broader economic data. This helps investors narrow down their investment choices - but at this stage would continue to include significant margins of risk.

AutomateRobo-advisor recommends portfolio investing and rebalancing decisions based on defined investment strategies (directly augmenting oversight from investment managers)

Develop enhanced or new insurance proposition based on monitoring actual behaviours (e.g. telematics, driving behaviour, health sensors). Whilst this has consumer risks it does offer lower potential pricing.

Enhance insurance customer fraud detection by assessing behavioural data from multiple sources. Potential fraudsters can leave clues, including historic behaviour, that an automated system is better placed to trace, capture and analyse.

Predict risk of loan delinquency and recommend proactive maintenance strategies. Machine learning can respond swiftly to dynamic information sources.

Accelerate claims processing by automating processes - this might including automated customer recognition or data capture to speed activity up

Use geo-data gathered from multiple image sources (satellites, drones, etc.) and use machine learning to assess potential flood risk on an individual building basis. AI enables frequent model updates based on new risk assessments as they occur.

Automate building systems to reduce energy costs through AI-powered adaptive temperature and energy control. The challenge is to justify the investment costs against actual energy savings.

Automate interaction between insurance agent with insurance company through chatbot functionality. This will enable firms relying on (often widely distributed) human salesforces to provide scale-able, up-to-date support and advice in dealing with customer issues. Staff supported may be in-house, 3rd party or independent contractors.

Detect potentially fraudulent or nefarious users (e.g. individuals under sanctions, investigation etc) through pattern matching of structured and unstructured data on transactions from different sources, such as phone numbers, addresses, company directors and news reports (HSBC, Quantexa, Silent Eight)

Evaluate customer credit risk using application and other relevant data for faster and more efficient decisions - frequently with a turnaround time measured in seconds. This offers the potential for traditionally-underserved customer groups to be offered products.

Offer users alternative investment portfolio options based on user-completed risk profile survey (users must purchase/manage portfolio themselves)

Predict asset price movements based on greater quantities of data (e.g., social media, video feeds) to inform trading strategies

Automate credit risk profiling to support fundraising by small businesses through crowdfunding platform (Funding Circle). This enables reduction in borrowing costs for eligible SMEs.

Assess building damage from weather or natural disasters with drones and satellite delivered imagery. Especially useful for reinsurance markets in the aftermath of major events.

Use social media footprint to build predictive models for assessing individuals behaviour in the real world - specifically lifestyle behaviour and therefore insurance risk. This raises potential data protection concerns in markets like Europe.