EMERGING BANKING
“ WITH GREAT DATA COMES GREAT RESPONSIBILITY , AND FINANCIAL DATA OFTEN COMPRISES SENSITIVE AND PERSONALLY IDENTIFIABLE INFORMATION ”
ADAM LIEBERMAN HEAD OF AI AND MACHINE LEARNING , FINASTRA
Banking ’ s new business models are about spinning up new products and bringing them to market rapidly . Composability , where services are broken down into specific capabilities , enables this . It maximises freedom , speed and flexibility while connecting with an ecosystem of third-party offerings that further increases choice and boosts innovation .
Banks should look at some of the most successful B2B technology companies to understand the value of composability . These companies have taken a function and created an entire suite of software capabilities , accessed through a single platform – think Salesforce for CRM , or ServiceNow for workflow management .
Once a business is plugged into these platforms , they have everything they need , whether they choose to enable all the modules at once or gradually over time . And all without the burden of integrating , updating , localising and innovating , which falls to the platform provider to do .
For large banks , adoption of composable banking is likely to be incremental . They
cannot simply give up on their incumbent technology , but must progressively renovate in order to run down their legacy investments . Challenger banks , fintechs and non-banks will want to scale fast , and adopt specific capabilities that have been precomposed for specific use-cases such as SME lending or digital mortgages .
AI , DATA AND PRIVACY
ADAM LIEBERMAN HEAD OF AI AND MACHINE LEARNING FINASTRA
From the adoption of chatbots to ML-powered risk and decisioning models , the banking industry continues to progress at an exponential rate . As financial institutions grow their business and adopt new technology , their stockpile of data is growing at the same rate . However , with great data comes great responsibility , and financial data often comprising sensitive and personally