Incumbents are hampered by the massive structures and legacy systems, and struggle to keep up with the rapid pace of the digital transformation. On the other side, challenger banks struggle to gain consumers’ trust and often lack in compliance with the regulations.
Most of the digital strategies of both challengers and High Street banks are focused on consumer acquisition. Back-end activities on the contrary still suffer from outdated and time-consuming procedures, which affect the user experience and the customer satisfaction of private and business clients.
In addition, the implementation of the PSD2 regulation has opened the door to third-party operators with a high technological background such as Google, Amazon or Facebook. In such a competitive landscape, banks can no longer delay in upgrading their value chain, building a digital environment and adopting a Fintech culture.
Launch of Oplon risk platform
In June 2018, we launched Oplon Risk Platform, an Augmented Analytics risk management platform that can fully automate the counterparty creditworthiness assessment process.
With oplon we aimed at overturning the traditional credit risk assessment approach, characterized by error-prone, time-consuming and paper-based procedures. The platform organizes all the steps of the decision-making process in a straight-through workflow, automatizing all the analysis procedures and allowing to perform complex credit risk analysis within a single framework.
Big Data and Artificial Intelligence technologies are employed to analyze thousands of data simultaneously and to generate predictive analysis’ models able to forecast accurate exposure scenarios.
Algorithms automatically find the most significant correlations among data, disclosing unforeseen insights and offering users an intuitive an immediate understanding of risk probabilities.
We opted for a modular structure, which allows us to offer a flexible, adaptive and fully customizable solution, tailored on clients need. The platform is organized in analysis steps and provides a powerful array of AI-based analysis tool, the same used by Credit Rating Agencies to assess the companies’ creditworthiness, such as: sensitivity analysis, debt capacity, cash flow analysis, qualitative analyses, etc. The whole analysis process, from the pre-feasibility and due diligence to the final approval, takes just a few minutes and lead to output metrics such as final rating and Probability of Default of the rated entity.
In addition, oplon offers a comprehensive set of portfolio analysis and management tools, allowing users to choose whether to perform analyses on a single counterparty or to carry out massive analyses of a specific portfolio section to determine metrics such as the expected losses, the loss given default and the value at risk.
A transparent credit rating methodology
As official Credit Rating Agency (CRA) and External Credit Assessment Institution (ECAI), providing a transparent solution was a primary issue to us.
MORE applies Artificial Intelligence to analyze all financial and economic areas and provides an immediate assessment of the reliability of the audited entity, whether corporates or banks, including in the evaluation both quantitative and qualitative data.
Data and models integration via API
One of the main problems in risk management is the lack of data for the creditworthiness assessment. Risk analysis models are mainly based on companies’ public financial data, overlooking information included in companies’ private documents and files.
Internal data are a valuable source of information for risk management and prevention, as they provide a real-time flow of quantitative and qualitative information on the company performances.
Private data and additional analyses models can be included in the platform via API. oplon’s web-based framework enables users to add custom models, developed independently or upon request, and to set them up as analysis steps of the rating evaluation process. Likewise, all oplon features can be integrated into other operating systems, allowing companies to embed data and functionality in their internal application.
Using Artificial Intelligence and Big Data Analysis, the platform can include in the evaluation process both financial data and alternative data provided by non-traditional sources, which tends to be unstructured, unorganized and text-heavy. The integration of external and internal, quantitative and qualitative data (BP, financial and bank statements, Credit Bureau report, AnaCredit, etc.) allows oplon to nowcast scenarios not yet highlighted by the market trends and to identify in advance risky factors.