April 23, 2021 - written by Ellen Hefferan. Wednesday at the LSTA Ops Conference, Jason Boyer (Finastra), Nicole Henry (Loan Ecosystems), Dr. Lewis Liu (Eigen) and Ellen Hefferan (LSTA) discussed various ways to drive digital transformation and bring legacy systems together to simplify a fragmented landscape. Imagine if systems could sit on one single platform and talk to one another in real time? Reacting quickly to what one may have seen in this platform may have mitigated losses linked to the meltdown of Archegos Capital Management.
Well, there may be progress to that integrated end-goal! COVID has been a massive accelerant for the cloud journey. A silver lining is that today almost everyone has their data on the cloud rather than on premise. With cloud computing, applications are updated faster. Integration has been made easier using APIs that allow systems to talk to one another. Banks have used third party firms such as Microsoft, Google and IBM to assist them in cloud formation, which is more cost effective for both the bank and the vendor.
Before analytics platforms can run models for cash flow or credit risk assessment, the data residing in siloed systems must get to one place. Fintechs fuel integration because they build systems with open architecture. By having systems communicate with one another through APIs, data can be brought together. Artificial intelligence (AI) allows legal terms and qualitative data to be digitized thereby transforming it into structured usable data that an analytical system can process to facilitate decision making and risk assessment. The way to use numerical and qualitative data must be specific to the value you are trying to derive from them. Business intelligence (BI) is all about harnessing and analyzing the aggregated data that has been generated, transforming it into reports and dashboards, creating visuals of key performance indicators and insights to make informed smarter business decisions. The ability to combine historical and current data can provide a vision on what the future holds. A good indicator of how your business is performing is how quickly you can react and make decisions. AI and BI can create (and maintain) a competitive edge.
You might ask, “Which comes first, standardization or automation?” Most of us (including yours truly, who has shouted from her “standards” soapbox for years) would be on Team Standardization because automation is just easier with standards. However, with today’s technology tools, we should not rush to this conclusion. As an example, lawyers have utilized AI tools to learn which terms of a credit agreement are heavily negotiated, to understand which terms to give up and which have economic impact. This intelligence, learned through automation, has led to structured credit agreement templates for certain clients.
No one can argue that Distributed Ledger (DL) has had a successful proof of concept. It is attested to not only by anyone who invested in bitcoin in the early years but also evidenced by an ad on a London bus: “which Crypto coin should you buy?” A DL would allow a party to record a transaction and provide a level of security that is supposedly un-hackable. Sounds good, right? Our panelists suggest that applying DL or Blockchain to negotiated credit and collateral agreements is akin to shooting for the moon. But that said, they offered a glimmer of hope that by digitizing specific operating sections of the agreement, rather the agreement in its entirety, DL is possible, and payments could be executed via smart contracts.
That, my friends could be a major win. Making data available to loan parties in real time and in so doing remove faxes, emails and manual processes may be achievable if we focus on it. There should be one central database or portal by which lenders’ systems can access and consume permissioned data via open architecture and APIs. The market is ripe for change and Fintechs are open to collaboration. Isn’t it time to get on that London bus?
