Sanjay Prasad, Head of Capital Markets, Wealth Solutions and Insurance, USA, TCS BaNCS
The conventional narrative on back office data has for long hovered around – How long to keep data in the production database? How soon can it be archived, say, five, seven or nine years? How to create a single source of truth? How can we extract the right intelligence to create customer reports? And, so on. Back office data was seen as more of a compliance requirement with a need for secure efficient storage and timely access when needed.
Is there another way to view back office data? A warehouse of intelligence, perhaps?
Digital is the new Goal and Data is the new Gold. What happens if we put Data and Digital together and see them as two sides of the same coin?
Patterns and Point-in-Times
Don’t we all love it when our Smartphones tells us to switch off the morning alarms because it is a long weekend?
A simple yet intelligent action triggered by a point in time data analysis. Let us put this analysis on steroids. I tend to believe that hidden in the terabytes of back office data are wealth of insights in form of both specific or point-in-time information as well as patterns or over-a-period of time information. Both types of such data sets can tell stories and guide us with decision making. We have perhaps not yet fully imagined and visualized the kind of insights such data sets can provide, when subject to intelligent analytics tools that can slice and dice the data and present it in meaningful ways, and if such insights can be even monetized. For example, can an emerging retail investor be provided with the analysis of different outcomes when responding to a voluntary corporate event and some high level insights of trends on similar classes of investors? A pertinent example would be that of a rights Issue. Can the retail investor exercise their rights? Another case in point could be that of deciding on the optional dividend. Should an investor pick a cash or a securities option? Market prices allow recommendations for the most optimal decision. Digital technologies like AI and analytics can be leveraged to conduct a ‘mind-the-peer’ group data, and market price of securities to arrive at the most optimal advice fit for that investor.
Self and Assisted Healing
Back office transaction data can also reveal patterns. Can patterns found in historical data be used to prevent or delay the ‘breaks’ from appearing in queues, or if required that these are at the optimal time to resolve the problem? Can such patterns help us service our customers better with subtle reminders at the right time and not annoy them by following up too soon? Can we use similar data patterns to help customers with their standing instructions and send timely reminders for seasonal and periodic actions?
Feeds received from market and data providers arrive in parcels, which may be incomplete to start with but shortly thereafter, can trailing information flow in and resolve breaks automatically? Historical data patterns can be of great use and they can help us establish fine-grained thresholds for such breaks. Such pattern-based tolerance levels can prevent break queues from filling up and causing operational risk, demonstrating a view of self healing.
A similar situation arises when rights on Intermediate security are not announced and the system can be configured to self-heal based on historical patterns via an auto-set up of a dummy security and thereby maintaining STP as opposed to creating a break. This highlights how systems can learn from users’ past actions and automatically take remedial actions the next time. Another elementary yet useful example of such self-healing breaks can be found by looking at past customer behavior during elections and providing stronger escalations and default options for clients that are frequently late on instructions.
Data is at the core of every digital enablement goal and data patterns and analytics from a sea of back office information can help with targeted deployment of NLP and Conversational UI tools for ‘assisted healing’.
In conclusion, intelligent harnessing of Data can surely help both digitalize and transform the back office of financial institutions around the world, mitigating risks and elevating customer experience.
Disclaimer: Views or opinions represented in this blog is based on author’s own research and does not represent TCS BaNCS.