Financial institutions and banks need to have the ability to scale their businesses through technology.
Doing so can increase efficiency, reduce processing times, and lower costs, thus improving the quality of processes and decisions, and ultimately increasing customer satisfaction.
The digitalisation of existing processes for credit and limit decisions offers a foundation for banks and financial institutions to achieve those goals.
Automated and customer-specific decisions are in high demand. Modern decision engines are available to achieve efficiency and meet the growth targets of banks and financial institutions.
Digital acceleration in finance
The COVID-19 economic crisis has accelerated the need for the use of IT-supported decision-making systems and automation.
It is now clear how important it is to flexibly and quickly adapt rules and calculations for credit and limit decisions and the monitoring of credit default risk.
Applied rules and regulations in decision making must meet regulatory requirements. Transparency and traceability are relevant for audits.
Adjustments of rules and regulations to the macroeconomic credit risk situation, and to the business policy of banks and financial institutions, must be tested in simulation procedures and must satisfy stress tests.
The performance of decision machines is essentially dependent on the availability of valid data.
Clear identification data on the business partner and debtors are essential for credit and limit decisions.
The business partner data must comply with the KYC checks, and borrower units must be formed. On this basis, clear risk assessments can be applied.
For credit and limit decisions, a distinction is made as to whether risk mitigation provides for hedging through credit insurance.
The automatic processes for accessing data from credit rating agencies and trade credit insurances are controlled accordingly in workflow management and decision engines, and run through the designated rule sets and scorecards for decisions and monitoring.
Their own experience with a borrower and debtor is of particular importance in credit risk engines.
Payment experience is of outstanding importance in this context.
Learning through payment experience
Payment experiences can be used to establish pattern recognition on experience values, and thus to derive predicted default probabilities and liquidity forecasts.
Mathematical and statistical methods are applied and can be further developed through the use of artificial intelligence (AI) and machine learning (ML).
The application of artificial intelligence and machine learning in the context of credit and limit decisions of banks and financial institutions is determined by the volume and the statistically necessary structure of data sets.
Artificial intelligence in the application of rule sets in decision engines is not new, and has already been practised for a long time.
Risk-based decision-making systems require sufficient valid data to apply artificial intelligence and machine learning to measure and monitor the risk situation.
Financial data – such as annual financial statement data and payment experiences – are particularly suitable.
In the future, social media data and Internet of Things (IoT) data can provide additional relevant information that is not available in classic external data sources, such as data from credit rating agencies and trade credit insurances.
Necessary technologies for the digitisation of information – such as optical character recognition (OCR) – and transformation into structured data are available.
The transfer of data through modern interface technology – application programming interface (API) – for the application of analyses and machine learning is also available.
Why use AI and ML anyway?
The goal of using artificial intelligence and machine learning is to optimise decision modelling to reduce bad debt, improve liquidity, and improve portfolio risk management.
For banks and financial institutions that have to make purchasing decisions on receivables and portfolios, modern analysis and forecasting methods can mean a quantum leap for risk-dependent pricing, conditions, and financing.
Banks and financial institutions need self-service to easily and straightforwardly set and change sets of rules and immediately evaluate their impact.
They can perform alternative calculations with existing and new data by changing the set of rules, which is necessary for regular validation of scorecards.
This also includes the transparency of rules and of the calculations for audit-proof documentation of the changes.
Banks and financial institutions benefit from just-in-time adjustments commercially, and therefore from speed and efficiency to improve time-to-market.
Urgent data needs
To conclude, credit and limit decision engines must therefore start from valid single-point-of-truth data sets.
Fast data access and fast updates of relevant data sets ensure reliable risk-dependent credit and limit decisions.
The increasing use of structured data and image data, processed in the growing use of OCR solutions, is leading to the increasing use of decision-making machines using artificial intelligence and machine learning.
Banks and financial institutions see more and more concrete application areas for modern decision engines to improve credit risk assessment, default forecast, limit allocation, liquidity forecasting, debt collection, and fraud prevention.
Beyond credit and limit decisions and fraud prevention, environmental social governance (ESG) is one of the central topics in the future.
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