Scaling up requires a more robust, data-driven approach to credit risk management, one that evaluates risk across a variety of customer demographics, geographic footprints and economic conditions. Analytics can help FinTech rms quantify, measure and predict risk.
Establishing a standard for managing risk will result in organisations incorporating risk considerations into their decision management platform and predictingoutcomes and scenarios. However, greater scale produces greater regulatoryscrutiny and a greater cost in both money and reputation; any mistake could result inan unfair or discriminatory outcome for a customer.
FinTech firms need to look beyond structured data such as credit score reports to the unconventional, like loyalty card consumer data to increase the accuracy, reach and predictability of credit risk models. Advanced data models will ensure that risky business models are uniform, enhance the quality of data and provide greater agility to address unconventional data requirements.
Transactional data is powerful, and analytics can also play a part here to generate simplified financial statements, affordability ratios, customer- and supplier-concentration analyses in real time, offering more up-to-date insights about company performance than annual accounts. With PSD2 and open banking now in force, similar analyses can now also be performed on new customers, in turn, generating new revenue streams.
Again, ensuring that the right platform is in place is key for future innovation,scalability and resilience. While the cloud allows for this and opens up the options for financial services players to choose new solutions for mitigating risks, the cloud’s ‘pay-per-use’ model is beneficial for FinTech firms of all sizes,allowing for a prioritisation of improving customer experience and focus on generating revenue.