CTO's, Product Directors and all varieties of tech gurus are eager to find use cases for AI under their remit; capturing a market or increasing their profile within their businesses. What they/we risk doing is artificially creating use cases that do not benefit the organisation, users or customers.
Our customers often talk with us about AI, and how they might use AI to its most significant benefit. Remember, AI and Big Data are not constrained by traditional structured data; they are enriched by non-structured data and information from sources that you would not normally expect, such as social media being used to make payment activity predictions. Robotic Process Automation (RPA) in the form of a chat-bot added to your customer portal might reduce the number of calls to support teams but does it add real value?
AI can add significant value to the sales desk, not just as a glorified chat-bot or a more powerful “Algo”, and not only for retail customers but also for the corporate customer.
So how could RPA & AI help your sales desks?
1) AI for volatility protection
The combination of AI and Big Data opens up the ability to monitor news feeds and open data for events that may affect the volatility and switching between volatility conditions. This may alleviate the need for one or more human beings to be sat monitoring the likes of Sky News, Bloomberg, the Weather Channel or the Shipping Forecast. AI can be on the lookout for events that indicate changes in the state of specific volatilities then react accordingly.
2) Understanding a corporate customers relationship with you
By looking at the customer's interactions with the bank, not just their transaction activity, AI can be used to predict patterns in the customers behaviours and offer more suitable products. A process that Sales Traders and Relationship Managers cannot easily complete due to the sheer volume and depth of data.
3) Understanding a corporates relationship with the world
In addition to understanding the customer from the Bank's stance, AI and Big Data can be used to more effectively predict behaviour based on how the customer's business is affected by events. Having the ability to predict when global and local events impact a customer segment such as transportation or manufacturing and anticipating their needs so that suitable products & services are ready and waiting for them can be the key to increasing stickiness & profitability to the Bank.
4) Reducing support overheads
Not only can AI and Big Data help increase sales desks profitability, but it can also help minimise expense across support functions, directly impacting your bottom line. By deploying AI within the support team environment, common requests from customers can be handled by Bots pre-trained to help. As time goes by, AI can detect new common issues and resolutions, reducing the number of interactions requiring human intervention.
5) Reducing error rates
Understanding the customer and its business environment will help increase retention and create the opportunity to cross-sell. Understanding your Sales Traders behaviour can lead to reductions in expenses caused by errors, and in some cases, inefficient behaviours. Some banks have already deployed internally-focused AI within their organisation to predict relationships and trends within the trading room. Being able to pre-empt potential errors and correct them, therefore reducing re-key efforts can increase STP.
6) Increased auditability
The auditability of decisions made by any human being is limited. We are all influenced by external factors, and AI can be constrained only to consider a limited common set of factors for all decisions it makes. Humans cannot. AI is able to document a full record of the decisions it makes and the logic tree used to make the decision. This auditability will ensure compliance officers are satisfied that due diligence has been achieved and the customer treated fairly. Because of this, much like the treatment of black box Algos under MiFID, black box AI is frowned upon as its decision-making process is hidden. The black box AI must, therefore, be written to take account of changing market conditions and market structure, regulatory constraints, and clients’ multiple objectives and preferences.
So, while it will be some time before the world is introduced to its first “Robo” sales trader, it won't be long before all sales desks are assisted by AI. According to a survey of 700 European companies by “DataBench”, an EU funded project, 43% of financial services companies are already adopting big data, with 2/3rds of those expecting to increase their overall spend in the next year. When looking for useful applications of AI within your sales desk infrastructure, it is worth considering your users as well as your customers as targets. Costs & efficiency savings can be made in addition to increasing profitability.
