Leveraging AI and machine learning in the insurance industry provides unmatched power in helping insurers with a multitude of problems and use cases. Whether it’s mitigating risk from fraud or customer churn, underwriting, pricing modeling, AI and machine learning can automate, accelerate and optimize every aspect. dotData can accelerate these processes and allow for development times that are measured in days instead of months.
Accelerate AI in Insurance
Driving Insurance Profitability with AI
Whether it’s building pricing models that accurately account for reserves, or creating risk-specific pricing models that can provide better market flexibility and increased profitability, AI and machine learning are ideally suited to automating pricing modeling in the insurance industry. dotData’s ability to automate 100% of the data science life-cycle allows insurance companies to focus on results rather than on the typically slow process of data science.
Customer churn is one of the most worrisome aspects of customer management for insurance companies. When good customers churn, insurance companies often replace existing business with new, more costly customers that lower profitability. Understanding the drivers of customer churn and creating AI and machine learning models that can accurately forecast churn behavior can boost profitability and revenues.
Whether it’s lowering the risk of increased losses due to fraud, or understanding the causal effects of litigation and increased claims, AI and machine learning is a powerful tool in the arsenal of insurance companies to help lower the risks associated with managing customers. dotData helps accelerate the process of building impactful risk-assessment models by automating the tedious parts of the data science process.
Insurance Use Cases
Applications for machine learning for banking are as varied as the banking business itself. At dotData, we have worked with some of the world’s largest financial institutions and are ready to help you make the most from your AutoML investment.
With dotData, insurance companies can deploy a real-time, or near real-time threat analysis scenario to protect their portfolio.
Replace static statistical models for fraud detection by creating AI and machine learning models that rely on continuous streams of data that leverage learnings from previous fraud cases to identify threat signals that can be pursued before a more substantial problem arises.
Price optimization leverages the benefits of AI and machine learning to analyze historical costs, expenses, claims, risks, and profits and projects them into the future.
Insurers can optimize and adjust quoted premiums dynamically, often leading to increased customer loyalty and lower operating costs.
By leveraging demographic, psychographic and behavioral information, insurers can leverage AI and machine learning models to create more personalized offers, loyalty programs and recommendations that lead to higher conversions, increased product upsells and can increase lifetime customer value.
Different customer types can have radically different expectations for insurance products and services.
AI and machine learning models can leverage existing customer data to build predictive profiles that can match insurance products to ideal customer prototypes that lead to higher conversion rates, increase customer loyalty, and higher customer lifetime value.
Lifetime Value Prediction
Behavior-based AI and machine learning models can be applied to forecast cross-buying and customer retention.
Recency and frequency indicators are used to customer behavior to predict future income streams from customers and to analyze and optimize customer lifetime value across the entire client base.
Leverage customer questionnaires, analysis of past customer choices as well as demographic information to provide accurate and highly tuned recommendations for insurance products to consumers.
AI and machine learning based recommendation engines can increase new customer acquisition and provide increased upsell opportunities to existing clients, minimizing churn and boosting customer lifetime value.
Monitor and predict both pure as well as speculative risk by identifying risk quantification and risk reasons.
Whether your company uses a matrix analysis model or another machine learning model to assess and predict risk, leveraging AI and machine learning can lead to better forecasting of risk based on potential risk groups and profiles.
Claims Prediction and Processing
Modeling claims prediction and processing can be a complex and time-consuming exercise.
Forecasting upcoming claims, however, helps insurers charge competitive premiums that strike the perfect balance between profitability for the insurer and affordability for consumers. Maintaining a competitive edge in the marketplace is a core part of the claim prediction process.
The Right Product for the Right User
Start by selecting the product you need, based on your environment, your use-case and your need to “get dirty” with the details of your data science workflow.
AutoML 2.0 & Data Science Automation
Leverage a full GUI experience to automate as much of your data science workflow as necessary. Empower citizen data scientists and data scientists alike.
How dotData Helps Insurance Companies
Insurance companies must leverage their best asset – their data – to become and remain competitive. dotData ensures your insurance company remains competitive by helping you accelerate the AI and machine learning development life cycle dramatically.
Chief Data Officers
Get projects out of the lab
AutoML and data science automation can shorten development life-cycles for data science projects to days instead of months. By automating the work that happens before your data science team begins to optimize machine learning algorithms, dotData can take projects that once took six months and complete them in days. Whether it’s performing the “data hacking” necessary for ML, or automating the tedious work of feature engineering and machine learning model tuning, dotData can help.
Chief Data Scientists
The tools your team loves, without headaches
If your data science practice is like that of most fintech companies, it’s either under-resourced or over-tasked – or both. dotData helps accelerate the process of data science by automating the tedious tasks associated with data wrangling and feature engineering, the most prolonged and most painstaking parts of the machine learning development life-cycle. With dotData, your team can focus on results rather than on the slow process of developing features.
Chief Information Officers
Data science without the headaches
When more than 96% of data science projects never leave the lab, the pressure to deliver on AI and machine learning projects can grow exponentially. Part of the solution is to provide your data science team with automation and acceleration tools they will use. The other answer is to enable an entirely new class of “citizen” data scientists to handle the 80% data science projects that consume precious resources but provide lower value. dotData can help with both by offering platforms that can automate data science for your business analysts while delivering your data scientists tools they will use.
AutoML 2.0: Data Science Automation Accelerates Your Business
Scaling a data science practice is challenging, time-consuming, and expensive. With Automated Data Science, you can empower data analysts, software engineers, and BI professionals to build and benefit from predictive models. Through Data Science Automation, you can embed models into applications seamlessly, while freeing up the time of your data science team to be more productive.
BI & Data Analysts
Unlike traditional AutoML systems that require users with in-depth knowledge of data wrangling and constructing feature tables, dotData automates 100% of the data science process. With dotData and minimal training, your BI team and data analysts can quickly learn to contribute to your ML and Enterprise AI initiatives, freeing precious resources and accelerating time to market for your AI & ML initiatives.
Data scientists spend 80% of their time in wrangling with data and constructing complex feature tables. By automating the entire workflow, you can liberate your data science team from the mundane tasks associated with data science and give them the power to provide tangible value to your business in a scalable, seamless manner that is not possible with hand-coded approaches or traditional AutoML platforms.
IT & Software
Integrating your AI and Machine Learning models into production environments is a crucial step in deriving value from your Enterprise AI projects. dotData gives your IT and engineering teams a seamless API-based integration model that enables Continuous Deployment and makes deploying and maintaining models fast and straightforward.
Executives and Line of Business
Giving executives and line of business leaders insights into the Machine Learning and Enterprise AI process provides the transparency and line of sight needed to keep projects moving and provide discernible ROI and value for the organization.