70%+ of Large Life Insurance Carriers Already Use Predictive Analytics

Deloitte’s 2022 Insurance Industry Outlook revealed that 67% of insurers are planning to increase investments in data analytics technologies in the coming years.

The latest advancements in data analytics techniques allow to further improve the speed, accuracy, and reliability of insurance forecasts. 70% of large life insurance carriers and 40% of large health insurers already employ predictive analytics in their business operations. Among midsize companies, predictive analytics adoption is around 20–67% with the projected growth of 20–30%+ across various insurance market segments.

8 Key Use Cases for Predictive Analytics in Insurance

Risk assessment and underwriting

Insurance pricing

Insurance claim triaging and settlement

Insurance fraud detection

Proactive prevention of claim cases

Insurance product optimization and promotion planning

Delivering personalized customer experience

Financial planning and analysis

Predictive Analytics in Insurance: How It Works

Insurance companies use intelligent predictions to identify complex dependencies and patterns in their business and customer data and precisely forecast particular future events, transactions, and performance metrics.

Predictive analytics for insurance help make optimal decisions across insurers' business processes, minimize financial and operational risks, identify opportunities for growth, and ensure high value of insurance services for customers.

Data science models used in predictive analytics for insurance

Statistical models

Process the available numerical data and offer trend-based calculations of future insurance metrics.

Best for: predicting stable quantitative insurance KPIs.

Price: $$

Non-neural network (non-NN) machine learning models

Process multi-dimensional structured insurance data, predict a wide range of insurance variables (e.g., risk, demand, revenue, expenses) based on the analysis of diverse factors that impact each particular variable.

Best for: batch predictive analytics.

Price: $$$$

Deep neural network (DNN) models

Deal with massive amounts of structured and raw insurance data. DNN models automatically determine change factors for the required insurance variables and deliver accurate predictions based on the identification and analysis of complex non-linear dependencies between these factors.

Best for: real-time predictive analytics.

Price: $$$$$

As the cloud and AI technologies are becoming more accessible and affordable, DNN-based predictive analytics solutions are being adopted more and more widely in the insurance industry. This lets the insurers make the most out of the available data and get near real-time, highly precise forecasts with minimal manual efforts.

A Sample Architecture of a Predictive Analytics Solution for Insurance

ScienceSoft’s experts consider the DNN approach the most effective for solving insurance forecasting tasks. Below, we describe the architecture our team typically employs to create powerful predictive analytics solutions for insurance.

Architecture of a Predictive Analytics Solution for Insurance - ScienceSoft

The essence: Real-time and batch insurance data from the available sources are processed and analyzed in two separate flows. A pre-trained DNN automatically produces highly accurate forecasts on the required insurance variables. The received predictions then get stored and visualized to be used by the insurance teams for operational and strategic planning. Real-time insights are sent directly to the relevant insurance systems to instantly trigger certain events, e.g., notifications about the insured assets’ pre-failure conditions, alerts on fraudulent transactions, dynamic premium changes, or claim payouts.

With the proposed architecture, you get:

Fast development due to the ability to build various layers simultaneously.
Optimized fees for the cloud (PaaS and IaaS) services due to the separate analysis of batch and stream insurance data.
Enhanced safety of insurance data and its easy recovery in case of system issues due to having dedicated storages for raw data, enriched data, and analytical results.
Flexibility to independently upgrade and scale each layer when needed.
Minimal human participation in model management due to continuous model self-training and no need for manual cleansing and structuring of input data.


	SCIENCESOFT'S EXPERT NOTE: The proper data governance and security are essential for safe and uninterrupted insurance data processing. Here at ScienceSoft, we help our clients set up a scalable, analytics-focused data management framework to ensure seamless data validation and correction. Plus, we implement robust infrastructure security mechanisms (SIEM, DLP, firewalls, IDS/IPS, DDoS protection tools, and more) to guarantee full protection of sensitive data and the IT infrastructure at large.

A Tech Stack to Implement Predictive Analytics for Insurance

ScienceSoft's teams typically rely on the following technologies and tools to design and launch predictive analytics solutions:

Data bus / Aggregation layer

Storage / Data Lake

Data enrichment and analysis

Stream data

Amazon Managed Streaming for Apache Kafka

Batch data

Data warehouse

Machine learning programming languages

Machine learning frameworks and libraries

Frameworks

Libraries

Machine learning platforms and services

Data and ML model versioning

Serving layer / Data storage

Serving layer / Data visualization

Data governance

Security mechanisms we work with

Data protection: DLP (data leak protection), data discovery and classification, data backup and recovery, data encryption.
Endpoint protection: antivirus/antimalware, EDR (endpoint detection and response), EPP (an endpoint protection platform).
Access control: IAM (identity and access management), password management, multi-factor authentication.
Application security: WAF (web application firewall), SAST, DAST, IAST (security testing).
Network security: DDoS protection, IDS/IPS, SIEM, XDR, SOAR, email filtering, SWG/web filtering, VPN, network vulnerability scanning.

Alex Bekker

ScienceSoft’s Head of Data Analytics Department

In predictive analytics projects, the dependence is clear: the less insurer involvement is needed to operate an analytics solution, the more manual effort is required at the development stage. Building software that seamlessly handles unique analytical operations and provides stable and secure integrations with the required systems is impossible without custom coding. Plus, implementing ML-based analytics involves manual design, training, and fine-tuning of analytical models.

Selected Success Stories from the Industry

Predictive Analytics to Innovate Transportation Insurance

Use case: Protective Insurance, a large US-based transportation insurer with over 90 years of expertise in the field, implemented predictive analytics to prevent claim cases. The Azure-based predictive analytics system enables real-time collection and processing of telematics data from the insured trucks. It relies on ML-powered analytical models to accurately predict potentially dangerous driving conditions and instantly notify drivers of the proper safety measures. Plus, with the powerful business intelligence capabilities, claim professionals get the previously unwieldy data sets elegantly displayed, which makes the process of reviewing potential claims more efficient.

Key techs: Azure Synapse Analytics, Azure Databricks (AI), Azure Data Factory, Azure Data Lake Storage, Power BI

Next-Gen Predictive Analytics for Commercial Insurance

Use case: Insurity, the US leading insurance analytics provider since 1985, launched a secure predictive analytics platform to help its clients from the commercial insurance domain improve their risk management, pricing, and policyholder servicing operations. The solution instantly analyzes customers’ data and introduces data-driven forecasts on the probability of loss. The obtained insights help promptly set optimal risk-based insurance prices and make accurate underwriting decisions. Also, the insurers receive ML-based predictions on the insurance portfolio health metrics. It helps them promptly take the appropriate risk mitigation steps and improve portfolio profitability.

Key techs: Snowflake, Amazon EBS, Amazon ECR, Amazon ECS, Amazon EC2, Amazon S3, Amazon GuardDuty, AWS CloudTrail, Amazon Machine Images, Amazon RDS for PostgreSQL, Amazon CloudWatch, AppDynamics, Rapid7, Splunk.

ScienceSoft: We’ve Been Here since the Dawn of AI Technology

Since 1989 in data science and artificial intelligence.
Since 2005 in business intelligence and data warehousing.
Since 2012 in insurance software development.
Since 2013 in big data.

Boris Shiklo

ScienceSoft’s Chief Technology Officer

It’s the innovative approach and high proficiency of our 750+ IT talents that help us deliver state-of-art predictive analytics solutions. We are proud of our passionate team of senior project managers, business analysts, software architects, developers, and data scientists that have 7–20 years of experience and hold deep expertise in the insurance domain.

How ScienceSoft Can Help On Your Predictive Analytics Journey

ScienceSoft designs and builds robust predictive analytics solutions that help insurance businesses drive value from the ever-growing volumes of data coming from corporate apps, third-party systems, social media, IoT sensors, computer vision cameras, and more.

Predictive analytics consulting

We advise on the best-fitting predictive analytics model for your specific needs, design a high-performing architecture for your solution, introduce an optimal tech stack, and deliver a detailed implementation roadmap.

Go for consulting

Predictive analytics implementation

We design, develop, test, and deploy your predictive analytics solution. Our service may cover the design, training, and tuning of ML models, including DNN models. We also provide continuous support and evolution of analytics software.

Go for implementation

It's High Time to Use Predictive Analytics Solutions for Insurance

	They improve business efficiency With predictive analytics, insurers can achieve up to 10% improvement in loss ratio, 5% decrease in claims costs, and 3x revenue growth, compared to the industry average. According to a recent ROI study, 25 insurers that employed predictive analytics realized around $400 million of incremental profit over five years.
	They speed up the insurance processes With the help of predictive analytics, the underwriting cycle can be accelerated by 25–40% and the time for insurance claim settlement can be reduced to just 3 seconds. The ability to provide fast and accurate insurance services leads to the increased customer satisfaction and retention.
	They drive innovations By employing predictive analytics solutions to capture and analyze IoT big data, insurers can leverage new and more effective business models, such as usage-based car insurance, parametric insurance, pay-as-you-live life and health insurance, and more.
	Predictive analytics market witnesses continuous growth It is projected to increase from $12 billion in 2022 to $38 billion by 2028 at a CAGR of 20.4%.