Machine Learning product deployment is difficult and takes time.
As business changes, redeploying working ML models becomes even more difficult.
HAKsys's Continuous Automated ML deployes fast.
It requires very little maintenance and intervention in production to produce high accuracy results continuously.
HAKsys does this by supporting the entire data and analytics pipeline continuously by its:
- its ability to integrate to different data sources easily
- automated data cleaning, transformation and selection procedures
- advanced modeling enabled by automated configuration of hundreds of different machine learning models
- continuous machine learning pipeline to support continuous business
- high performance and scalability on real time data streams
- augmented intelligence enabled by understandable models that can learn from data and humans, continuously
Continuous Automated ML for Insurance
Profit and Loss Detection and Prediction
Challenge:
The insurance company has different profit and loss rate in different micro-segments but the nature of those segments or reasons for profits/losses are unknown. If the company can discover where it is profitable automatically and continuously, it can examine the nature of those micro-segments and use different marketing and pricing strategies.
Solution:
HAKsys takes as input offered and earned premiums, profit and loss values (estimated and actual), market and competitor data and external economic data. Multiple definitions of profit and loss (e.g. w.r.t. previous month/year, greater than a certain threshold) are also handled according to business preferences.
Result:
HAKsys ML models are updated continuously and are accurate as the world changes. Thanks to understandable models, the company is able to detect previously unknown micro-segments with different profit/loss patterns and plan business actions. Business can work directly with HAKsys models and update them according to their current marketing decisions as well as domain knowledge.
Claim Frequency and Severity Prediction
Challenge:
Frequency and severity of different types of claims is the most important factor that determines profitability of the insurance company. The company would like to determine high and low claim frequency and severity micro-segments so that it can examine the nature of those and use different marketing and pricing strategies.
Solution:
HAKsys takes as input claims data that include driver, vehicle, location, repair information, and historical claims data. Claims in different categories are handled separately.
Result:
HAKsys ML models are updated continuously and are accurate as the world changes. Claim frequency and severity can be predicted for new types of vehicles and drivers. Thanks to understandable models, the company is able to detect previously unknown micro-segments with different claims patterns and plan business actions. Business can work directly with HAKsys models and update them according to their current marketing decisions as well as domain knowledge. Claims are processed faster.