Non Performing Exposures (NPE) need granular position management, requiring industry expertise and legal skills. This involves distinguishing between going and gone concern companies, in order to pursue the recovery or support the back to bonis strategies through the identification of optimal financial solutions. Quality of information and the speed of retrieval are crucial to avoid that delicate underlying situation which can lead to a sudden state of default and therefore to loss of the value of an operation.
Currently, the onboarding phase is mainly a manual process that requires the non-performing Loans (NPL) office operator or the third-party supplier to open each document contained in the NPL portfolio to assess relevance and classify significant information. In case of large portfolios, the process can take many months to complete.
NPL Market players (Banking Institutions or Servicers) can activate several action plans to extract more value from NPL management, which can enhance the process and make it more valuable. One of the most important intervention areas relates to “recovery timing”. The effectiveness of the recovery machine in terms of timing and recoveries is crucial to extracting more value from the process. Recovery procedures tracking, collections monitoring, effective recoveries qualification are all elements that can be leveraged to increase value.
From a technology perspective, several elements can enable process transformation and value extraction. The first set of enablers, made available thanks to new AI and ML technologies, are so-called “smart” engines. Engines can be defined as “smart” when they rely on feeding data to improve performance each time they are used, or by applying Machine Learning.
Thanks to our regulatory knowledge, business expertise and technical skills, Reply has designed and developed a “NPL Data Room” solution, designed to support the onboarding process for NPL management. The solution transforms manual process by automating the activities performed by NPL Loan Managers, leveraging ML and AI technologies, and transforming the NPL onboarding phase.
Reply’s solution frames the ML and AI algorithms within a versatile architectural framework, enabling value in the semantic analysis context. In particular, it’s able to reduce the processing time, automate standard activities, lower Third-Party Provider (TPP) costs, and reduce TTM for due diligence, validation & workout.