July, 2nd, 2018
Marjane Mabrouk, Finance Division Manager
From real-time market analysis to fraud prevention, automation of internal processes and regulatory reporting… Big data and artificial intelligence are adding complexity to occupations in the banking sector.
The increasing influence of FinTechs is forcing the historic players in the financial sector to accelerate the pace of their transformation.
Both the specialist and general press have written at length about the digital transformation taking place in retail banking. Although it has come later and received much less media coverage, the revolution that is currently underway is also affecting the B2B/investment banking, private banking, trading roles and asset management activities of the major financial institutions. In recent years, investment banks have been involved in a string of big data and artificial intelligence (AI) projects.
CHANGES IN THE FRONT OFFICE
The main focus in the front office is on improving knowledge of the customer and anticipating market behaviours. The new data intelligence platforms can analyse market trends in real time, in order to optimise trading strategies while reducing exposure to risks. This predictive analysis is made possible by the computing power and infinite storage possibilities available in the cloud. That said, digitisation does not mean the end of traders, asset managers or brokers. They will not all be replaced by robo-advisors. Some roles, such as debt-capital advisory, will still need human skills. Other areas, such as foreign exchange, will see a more significant intrusion by robotic systems.
MIDDLE AND BACK OFFICES ALSO AFFECTED
From a middle-office perspective, big data and AI tools are being used to manage business risk and to automate internal processes. Powerful algorithms are helping to combat money laundering and fraud (AML: Anti Money Laundering), making it easier to detect human errors and improving cybersecurity, by listening continually and more deeply to an increasing number of signals. At the same time, algorithms are continuously improving the reliability of the surveillance model for potentially high-risk transactions. This is allowing the banks to expand their fraud-detection capacity without incurring unacceptable costs. For back office functions, AI is an amazing lever for “intelligently” automating manual, repetitive, time-consuming tasks and thereby reducing operating costs. While best practice guidelines, such as Lean Management or Six Sigma, have already helped to optimise processes, AI goes a step further. It continues and expands on the Robotic Process Automation (RPA) movement that began a few years ago. Finally, big data and AI are helping the banks to comply with regulations. The fine granularity of the data analysis available helps to improve the quality and speed at which regulatory reporting is generated. These new technologies have helped to tackle the challenge of MiFID II (Markets in Financial Instruments Directive) and will support the banks in the process of ensuring their compliance with other regulations, such as the FRTB (Fundamental Review of the Trading Book), which will come into effect on 1 January 2020.
A.I. LEADS INVESTMENT PRIORITIES
These innovative projects are set to multiply. According to a study by consultancy firm PWC, AI (30%) is leading investment priorities in the financial services sector for 2018 with regard to emerging technologies, ahead of identity and biometrics management (20%) and blockchain (19%). The contribution made by these new technologies is well timed for corporate and investment banks. It offers a breath of fresh air given that, for the last few years, these banks have seen margins drop in a tense economic situation, which has prompted some of them to reduce their number of staff.
As well as the competitive advantage and productivity gains they offer, big data and AI are helping commercial and investment banks to provide better coverage for certain segments of their customer base. For example, a detailed analysis of large volumes of data is allowing commercial banks to respond more effectively to requirements such as trade finance, foreign exchange and financing) among SMEs and intermediate-sized businesses, while they had previously been highly focused on major corporate clients.
Another incentive is data sharing. New, external data suppliers, such as CustomerMatrix and traditional players like Bloomberg and Markit, are now offering data that help improve customer knowledge (KYC) and manage the associated risks more effectively. The banks may, for their part, be more inclined to pool their IT assets, with the aim of reducing the acquisition cost associated with data processing and of improving both the actual processing and quality of outputs. Thanks to AI and big data, data processing and distribution will increasingly be done via external or shared services.
BANKS AND FINTECHS: A LOVE-HATE RELATIONSHIP
Of course, these changes are also taking place in response to pressure from the FinTechs. These specialist start-ups are positioning themselves as competitors to corporate and investment banks or as service providers. They have prompted the investment banks to reflect on what they do and speed up the adoption of new technologies. This has also encouraged them to create dedicated innovation structures, with the appointment of a Chief Data Officer or Chief Digital Officer. As this stage, competition from the FinTechs does not constitute an economic threat. Although they are experiencing strong growth, their level of income is still negligible compared with players in the finance sector. Conversely, what the FinTechs have on their side is their capacity for innovation and their agility.
The banks may, for their part, be more inclined to pool their information assets, with the aim of reducing the acquisition cost associated with data processing.
Banks and FinTechs have every reason to work together. The former are using the latter to drive innovation internally and enhance their offering. Banks are accelerating their acquisition of innovative skills and ready-to-use solutions. FinTechs also want to build closer relationships with the banks to use them as incubators or potential buyers. Closer relationships of this kind are allowing them to secure additional investments, to deploy their solutions on a large scale and to benefit from their understanding of the banks’ long view and knowledge of their customers. Moreover, many FinTechs are set up by traders who have opted for a change of direction, or former investment banking executives. They know the strengths and weaknesses of their former employer and can get in through the back door. And it is easier to set up a new model from a blank page than from within an institution that has existed for 50 or 100 years.
JEANS AND TRAINERS IN THE HEAD OFFICES OF MAJOR BANKS
These young people are itching powder for the historic players in the world of banking. The newbies are forcing the older generation to go faster. And it’s working. We are seeing a real desire among the banks to get on board with technology internally, with skills development for employees and people with unusual backgrounds arriving in the sector. It’s not uncommon to see young data science graduates in jeans and trainers working for the banks rather than in the start-ups one might have expected. The banks have improved their employer brand and changed their image to attract the particularly sought-after talents of data scientists and other machine-learning experts. From hackathons to coding competitions and meet-ups, they are developing all kinds of innovative recruitment methods. All these are positive signs. This is a reminder that the digital revolution is, first and foremost, a story of human resources in which transforming business models relies on bringing in people from different backgrounds and introducing more agile working methods.
Big data and artificial intelligence tools are allowing the banks to expand their fraud-detection capacity without incurring unacceptable costs.
ULTIMATELY, THE CUSTOMER WINS
In the end, the banks will adapt and buy other businesses and skills. They will make their platforms and information systems more open to control their costs, but also drive innovation with, perhaps, the emergence of new actors in banking roles. It is possible to imagine specialist rating agencies that will offer more relevant customer risk analyses in relation to KYC, AML or client screening, which will give the banks access to this kind of service at a lower cost than developing a system internally (thanks to economies of scale) and thus improve their own internal rating. All of this will be more streamlined and ensure greater transparency in assessing customers. Lower costs, refocusing attention on the client, transparency, creating internal or external utilities and accelerating production cycles are all elements that suggest a kind of industrial revolution in banking, like the one that has taken place in the automotive sector in recent decades.
Ultimately, the client is the big winner in the “coopetition” between the banks and FinTechs. Putting clients firmly back in the centre re-establishes the balance of power in their favour and allows them to be more demanding. The shift that we have been seeing in the retail banking sector for the last few years is happening in the same way in B2B, except for the fact that commercial banks are not under attack from the web giants. At least, not yet.