Every insurer today is investing heavily in transforming to a Digital savvy organisation led primarily by the motivation of providing superior customer experience, reduced costs and possibly newer business models.
Organisations are focusing on technology, rewiring their processes, and creating user friendly interfaces for a great experience. These are all important factors but the incremental long–term value will come from the seamless orchestration of Data to Decisions journey and the ability of an organisation toembed this process into every customer interaction.
When predictive analytical models are embedded into a business process to effectively produce instantaneous answers, you have successfully achieved Automated Decision Making.
The growing complexity and magnitude of managing potentially thousands of decisions needs something better than an old and inefficient handcrafted approach.
Some of the key capabilities that can help reduce time to value for the Analytics life cycle include:
• Data Preparation & Exploration
A strong Data Management tool is critical to building analytical data marts as well as help automate and schedule data pipelines to run in batch and real time. The solution should be capable to provide self-service data preparation, data wrangling and in-database processing to reduce data movement and improve performance.
• Model Development
❖ Programming Agnostic – Data Scientists today have their preferences on language used for model development. Having separate analytical platforms to support varied languages (SAS, R, Python), or havinga data scientist re-trained to learn a specific languageis expensive and inefficient. The model development platform should support all the major model development languages thus making it more versatileand flexible for the analytics team.
❖ Point n click development – Data scientists should have maximum degree of freedom to experiment with the data, modeling techniques and hyper tuning parameters. There could be hundreds of models and the data scientist should be able to generate such scenarios in quick time.
❖ Auto ML – Increasing number of models make itextremely tedious for the analytics teams to deliver all the model requirement. Automated ML can help cater to scale to these analytical requirements. Automated ML should have the complete capability for building a pipeline starting from imputation, feature engineering, variable selection and then selecting the best model. Itshould allow data scientists to override the parameters given by the machine.
• Model Implementation
❖ Model Registration – Post model development, analysts register a model package that contains the model, including all of the data transformations, imputations, etc., and associated output and documentation. This package ensures that the right steps have been taken, and that a suitable, powerful model is released into the production environmentwhich helps organizations standardize the process of creating, managing, deploying and monitoring analytical models.
❖ Governance – It is critical to have a centralized model repository, lifecycle templates and version control which can provide visibility into analytical processes and ensure that they can be audited to comply with internal governance and external regulations.
❖ Model repository – A central, secure repository should be able to store extensive documentation about the model, its scoring code and associated metadata and enable modelers to easily collaborate and reuse model code, with their activities tracked via user/group authentication, version control and audit controls.
❖ Scoring – Once a model has been reviewed, approved and declared ready for production, it has attained champion status. With the click of a button, the entire workflow of your champion model should be turned into scoring code that can be deployed in production.
❖ Faster deployment – The solution should enable the conversion of complete analytical scoring code into lightweight web services or native languages for processing in databases automatically.
• Model Management
❖ Automated model monitoring – Model performance jobs should be enabled to be scheduled on a recurring basis – and the results monitored on an ongoing basis – for one model or the entire model inventory. These results can be posted on dashboards and thresholds defined for performance. And if these thresholds are violated, analysts should be able to receive alerts about the models that need attention, saving time and money.
❖ Model documentation – To strengthen the model governance, high-quality model documentation has become increasingly important. Model documentation is a formal process to collect all relevant documents and data that provides a detailed explanation of assumptions, rationale, derivations, tests and other analysis for a model that’s is being deployed
Each of the above mentioned capabilities are critical for an Insurer to automate their Analytics Lifecycle and derive the potential ROI from their digital transformation investments.