Challenge: high manual effort in testing processes
In the area of claims assessment and settlement, the manual verification of various types of documents often results in high costs. In many cases, this verification is not economically viable, since either no reduction of the payment can be implemented or it is so low that the verification costs exceed this amount. In order to achieve this, information about the possible success of the inspection / the need for inspection must be made available as early as possible and used to control the work material in the sense of a light-dark control or case prioritization.
Increase of the dark processing rate by machine learning
By using modern mathematical-statistical methods (AI- / Machine-Learning methods) a higher automation of the process can be achieved. Machine learning algorithms are used to identify those calculations that promise the highest possible test success in order to prioritize them for manual testing. Thus, only those cases that are particularly worthy of being checked are sent to the clerks, the large mass of invoices is processed in the dark.
Operationalization: Putting AI into effect
The aim is to embed the model in the corresponding operational regulation process. The first step in this process is the construction of an operationally executable minimal model. Such a model can then be integrated, for example, following the corresponding rule check before it is sent to the administrator. From a technical point of view, such ML models are typically connected as microservices via REST APIs to the corresponding interfaces of the service systems. Depending on the prerequisites, integration into the testing process (call microservice and process control based on the model score) usually requires an IT implementation project.
Value contribution: what benefits can be expected from optimizing the service process?
The optimal control of cases to be checked as well as the optimization of the light-dark control in the document material reduces manual effort and thus leads to a higher degree of automation. It is possible to achieve higher reduction sums through optimized case checking by the available employees – the overall checking result increases.
In principle, this form of ML-supported process automation can be applied to numerous other use cases in the field of claims auditing and claims settlement. Please contact us at any time if you are interested!