How AI and ML Could Improve the Insurance Industry and Save Money

Source: Artificial Intelligence on Medium

How AI and ML Could Improve the Insurance Industry and Save Money

Insurance companies could offer better services by harnessing big data, AI, and machine learning!

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The take-up of A.I. has become a key feature in driving business changes across the insurance journey lifecycle. Early adopters use it obtain better lead scoring, higher conversion rates, more effective cross-sell and upsell, increased retention, and personalized AI-powered customer experience throughout. The following are features of organizations that are successfully adopting data and A.I. in order to foster change:

Empowering Actuaries to Become Citizen Data Scientists

Pioneers in the space are moving away from the crude averages that still provide the basis for many actuarial and underwriting models. New A.I. approaches allow companies to accurately depict multifaceted individual behavior and build optimal risk profiles specific to an individual or event. By automating and democratizing formerly technical elements of data science, SparkBeyond lowers the bar and provides the tools for actuaries to build and use sophisticated A.I. models.

Forward-leaning Executive Championing Data & A.I.

The first six to twelve months of adopting A.I. is usually decentralized with three to four use cases across one to two business lines. As use cases develop into solutions spanning departments and geographies, ownership centralizes under data-literate and progressive business leaders, or ‘forward-leaning executives,’ who are given the capacity and authority to drive digital transformation. While the ‘forward-leaning executive’ role often falls under the CDO or COO, one owner we found surprising yet impactful was the CFO — a position which naturally counsels the CEO with privileged business KPIs and a need to reinvent, as reporting-heavy responsibilities become automated.

Cross-functional teams

Analytics cannot and should not be conducted separately from the business context; tangible business actions can be elicited from data in order to change the processes to reduce risk and increase profitability. Multi-disciplinary teams that are diverse in function and capability embed data science into business strategy. The new wave of A.I. technologies serve a cross-functional team and are better equipped to drive business impact.

New Data: Wearables, Telematics and the ‘Internet of Things’

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Connected devices outnumbered people in 2008, and the data from these new devices is now giving insurance companies better insight into customer behavior. It’s even going so far as to influence behavior positively. The high-frequency data gathered from devices such as smartwatches are carefully analyzed to identify sophisticated patterns in time or space that are associated with safe and unsafe behaviors.

Collaborate to Reduce Risk

There is great potential for institutions to collaborate by sharing datasets that pertain to mutual features, such as improving outdated and inefficient operational processes, which often cross multiple business lines and product categories. This would give institutions the flexibility and increased capacity to execute differentiating factors such as strategy. This is achieved through SparkBeyond’s ‘blindfolded analytics’ process, where an institution can share a set of insights instead of the actual raw data that is so often disallowed by regulators.

Generate Business and Social Impact by Introducing Data and A.I. to Your Organization.

Sustainable transformations are driven by short-term tactical use cases within the context of a broader audacious vision for the future. Quick wins and rapid iterations complement a wider mission to reinvent the insurance value chain with data and A.I. in a bid to capture market share and margin. SparkBeyond’s clients have been successful in starting their journey combining a sizable impact of at least $3M (a specific use case in a specific line of business in specific geography) with a forward-leaning executive (who can scale the impact and become an early champion or change agent within an organization). Below is a suite of use cases that generate impact across the insurance value chain:

Introduce data and A.I. to the daily running of a business, with changes that can be made at a departmental level, in order to maximize ROI on existing products and services.

Distribution, Sales, and Marketing

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Retention management (churn): understand patterns behind customer churn or lapse in order to enable proactive retention and predict individual churn. PART III Generate business and social impact by introducing data and A.I. to your organization

Value-based prospecting (lead scoring): characterize high-worth clients for new business prospecting and incorporate patterns to score propensity of a new lead.

Cross-sell and up-sell: build a lead generation engine for cross-selling or up-selling.

Value-based marketing: optimize digital marketing spend by connecting predictive lapse, lifetime, and margin scores of new and existing customers.

Optimizing agents: improve promotions, direct the right leads to the right agency, and promote lower cannibalization between agencies.

Underwriting and pricing

Individualized risk assessment: leverage A.I. to assign risk by responding to the unique profile and properties of each individual (instead of assigning them to a group based on crude averages).

Automated underwriting: eliminate the need for manual follow-up (e.g., invasive medical tests in life policies) by better-synthesizing patterns in self-reported information.

Customer onboarding and third-party data: improve underwriting efficiency and operations by prioritizing data sources to use based on availability and cost.

Claims excellence

Claims automation: automate severity modeling given only initial or partial information available when a claim is submitted.

Claims steering: leverage value estimations and patterns from past behavior of similar consumers in steering towards a win-win resolution.

Fraud: deepen understanding of fraudulent claims to identify cases where the claim damage request far exceeds the actual damage.

Subrogation potential: Identify instances of uncollected damages from third-party involvement in claims.

Strategic: Become the Insurer of Tomorrow

Keep up with the pace of change by building on initial data and A.I. foundations to capture a larger segment of the customer value chain and further enhance margin and market share going forward.

Data Partnerships and Ecosystems

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Connected device partnerships: introducing telematics data into A.I. systems provides stronger risk assessment and drives more valuable dynamic pricing.

Cross-sell partner services to add customer value: deepen existing customer touchpoints and create new revenue streams by cross-selling services or products from partner organizations that add value — E.G. offering tour packages for customers purchasing travel insurance.

New Products and Revenue Lines

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Usage-based insurance: leverage data and A.I. as a muscle to accelerate sharing-economy specific product lines, such as insuring private cars when in use as taxis (in the case of Uber) or homes when in use for the purpose of hospitality (like Airbnb).

Long-tail markets: build new insurance products that can protect individual items or events.

Parametric insurance: with data and A.I. as an accelerator, insurance companies can build new products that offer immediate compensation based on public data no longer needing a claim.

A.I. solutions can improve the safety, security, and wellbeing of our customers. Strategic integration of new types of services based on A.I. makes a difference in the quality of life, generating new value for our partners and customers alike.