In certain ways, machine learning is crashing full-tilt into the financial services industry. After getting their feet wet with analytics tools that sifted through masses of data to gain business insights, banks are now levelling up with risk advisory robots that provide real-time analysis and insights for sales teams and relationship managers. This next level technology will also help predict client behaviour and identify market opportunities.
Nazneen Sherif writes about how banks can use machine learning to boost sales in this article from Risk.net:
Under pressure to cut costs, banks are all facing the same dilemma: how to do more with less. Many have embarked on transformation projects, leveraging big data to identify potential client interest. Now, some are going a step further by augmenting sales teams with machine learning algorithms that can comb through large datasets to flag up corporate risk management needs.
This is the world of risk advisory robots, which aim to put relationship managers on the front foot by absorbing information that might otherwise be missed and delivering analysis to drive new sales opportunities. All in the time it takes to make a cup of coffee.
“If you were to have analysts covering 75 assets and asking them to provide outputs every day, you would need a team of reasonable size. Whereas with this process we can have all that analysis done within minutes,” says Michael Sneyd, the global head of quantitative and derivative strategy at BNP Paribas in London.
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The development is part of a wider digital transformation taking place across the front office as banks find new ways to make the salespeople and traders quicker, smarter and cheaper. Big data projects have been underway since 2017, initially embraced by market-making businesses in an effort to identify customers for a specific trade idea or an axe. New technology, which combines pools of data from across the bank with external information to determine the best course of action for specific clients, is bringing that revolution closer to the world of risk solutions and advisory.
Depending on the bank, the information collected could range from external market data from public databases and vendors, to internal data on existing positions with clients and customer relationship management (CRM) systems. In the future some banks see this list expanding to include information from email communications, public news and speeches extracted using natural language processing techniques (NLP)
