How the banks want to use AI
1. Chatbots and virtual personal assistants WHAT DOES THIS MEAN?
Banks are using chatbots and voice bots to interact with customers and solve problems before any human staff get involved.
THE TECHNOLOGY BEHIND IT
Natural language processing and generation will make it increasingly difficult for customers to tell whether they are talking to a human or an AI interface. Voice recognition and facial recognition could be used instead of passwords to ensure security.
2. Profiling customers WHAT DOES THIS MEAN?
Banks want to offer personalized communications and decisions based on detailed profiles of each customer. They could also use customer profiling and algorithmic sorting to assess risks and precision-target offers.
THE TECHNOLOGY BEHIND IT
AI could use the vast mass of unstructured data on each person to profile customers. Machine learning — computers which can learn from data — could then be used to analyze behavior patterns. Algorithms could also automate increasing numbers of decisions. Language analysis will also be applied to word choice and syntax to predict decisions. This technology is already being used by some fund managers to assess the word choices of chief executives and work out what that means for future company performance.
3. Streamlining processes WHAT DOES THIS MEAN?
The banks want ‘low-value processes’ to be handled by AI. This would mean documents being scanned and parsed by computers. Some decision-making could be made by AIs operating with complete knowledge of the regulations and laws in each territory.
THE TECHNOLOGY BEHIND IT
Image recognition and machine learning could be combined to scan masses of documents, and take actions based on the laws and regulations which apply. Algorithms could then be used to decide which cases should be passed to a human decision maker.
4. Spotting patterns WHAT DOES THIS MEAN?
AI could spot the anomalies or patterns in transactions which might indicate fraud and money- laundering. Face and voice recognition could also flag up fraudsters who are already in the system. Data could be sifted to find trading patterns that indicate risks or investment opportunities.
THE TECHNOLOGY BEHIND IT
Machine learning enables AI to parse the masses of unstructured data to separate the signal from the noise in markets, and it can self-correct. Complex image recognition can be used to identify people and objects.
THE COST: JOB CUTS, SPENDING AND DEPLOYING AI
The FT surveyed 30 of the world’s biggest banks about their approach to AI. Eighteen provided detailed answers to at least five of the 14 questions, another five gave descriptive responses on their AI efforts, the rest declined to participate. The results show:
FRONT OFFICE IS KING:
17 of the 18 banks are already using AI in front office for everything from Citi’s Facebook messenger chatbot to UBS’s use of Amazon’s virtual assistant Alexa for customer service. Front office is also where banks see the biggest potential for AI-related savings.
BROAD IS BEST:
Eight of the 18 banks are using AI in front office, middle office, back office and data analytics. The other 10 are using it in three of the four areas.
RESOURCES VARY WIDELY:
Nine banks provided details on staffing their AI efforts. One European bank, which asked to remain anonymous, employs between 500 and 800 people. Nordea said it had 25. Six banks gave details of AI spending; the sums ranged from € 5 million to € 15 million, with one institution planning to increase spending from below $ 3m to $50m a year.
CONSERVATIVE ON JOB CUTS:
Seven banks gave estimates of possible AI-related job losses. Six said it would be below 20%. Seniors accountable: Five of the 18 banks have management board members directly responsible for AI. Partnerships in vogue: Eight of the 18 banks are involved in joint ventures, four have made investments in AI-related companies.