How artificial intelligence is transforming the financial ecosystem: A Swiss perspective - Banking blog

AI

Artificial intelligence or ‘AI’ – whatever we actually define it as - is transforming the way we do business in financial services: the increased importance on the scale of data, more customised offerings, and a more sophisticated interplay of humans and automation.

In a nutshell, AI and analytics enable five key capabilities – customisation (of experience and service, optimising outcomes), foresight (to predict what is likely to happen), decision making (to recommend or automate specific decisions based on the best outcome), interaction (between computers and humans) and pattern detection (to understand themes and regularities in context). In collaboration with Deloitte, in order to understand the impact of these capabilities on the global financial services industry the World Economic Forum released a report titled ‘The New Physics of Financial Services: Understanding how artificial intelligence is transforming the financial ecosystem

This report explores how AI – from insights through engagement to automation - is disrupting financial services and what it means for financial executives, regulators and policy-makers. We want to ask what this means for the Swiss banking market?

The report shows four key areas that banks need to be aware of in making the most of their data and integrating the required technology for AI. These challenges span across four areas: value creation, talent, competitive dynamics, and public policy.

Value Creation

For Switzerland, the reputation of stable long-standing financial institutions has been key to the acquisition and retention of customers. As banks are competing more globally for money, clients expect banks to approach them with personalised offerings. Online platforms enable clients to compare offerings, and fintechs such as Revolut and TransferWise are disrupting parts of the banking value chain. The power is shifting and banks must adapt accordingly.

Fintech firms in particular have the technological infrastructure already in place for AI and analytics activities and are at the heart of increasingly open banking regulations. Elsewhere in the world, tech firms (particularly the likes of Alibaba, Amazon, Apple Pay, Google Pay) are offering innovative financial services products to their customer base. Their strong technological foundations support the creation of self-driving finance offerings that automate routine transactions or advise customers on complex decisions such as home-buying or retirement planning.

Swiss banks are behind the curve in using data to understand their clients’ needs. This is caused by a large legacy landscape and processes focused on locking data down, not using it to generate insights. Leading banks are trying things out – Credit Suisse launched ‘Amelia’, a virtual agent, in collaboration with vendor IPSoft, to understand and resolve or redirect clients’ problems, and UBS created an economic forecasts service, leveraging Amazon Alexa, making it easier for clients to get the ‘house view’.

Talent

Banks have been significant hirers of technology staff for a long time – as banks decision processes become even more data intensive, the range of skills banks need, and where they place them, will evolve. Data scientists, neuro-linguistic programmers, neuroscientists, linguists, design thinking specialists, and decision modellers are all roles that are front line in the move to AI. Operating models and funding may need to change too. The traditional splits between ‘IT’ and ‘business’ and ‘Change’ and ‘Run’, while easy to understand, are not necessarily enough when data scientists need both business and analytics skills, and are working with real data sets and impacting real time decisions. Banks need to organise, recruit and train into these roles.

Competitive dynamics

As data and machine learning becomes much more of a crucial enabler of business outcomes, the scale and breadth of data available to use for training machine learning models becomes much more important. In other countries data sharing is becoming a popular dynamic; sharing of anonymised data within an industry or between institutions in different industries with an overlap. Even in the hyper-competitive ‘ride-sharing’ market, Uber and Lyft are collaborating to share data. With a change in mindset, along with of course sufficient anonymization of their data and sufficient policies and alignment with regulations, Swiss banks – particularly smaller local and regional banks – could collaborate to enable anonymous data sharing to drive their use of analytics and AI. Organisations such as the Swiss Data Alliance have already been established with has the aim of establishing a forward-looking data policy and encouraging open data in Switzerland. We need to see a significant change in the minds of bankers so that they understand the value of AI in serving their clients and managing risk - and the role of more comprehensive data sets in achieving this.

Public Policy

In the highly regulated Swiss environment, all banks face the same challenges in adhering to data-related regulations such as the General Data Protection Regulation (GDPR) that impact their ability to develop AI solutions and create data alliances. Swiss banks shouldn’t miss the opportunity driven by that GDPR and the Federal Data Protection act give to properly manage their internal data – too many companies have seen GDPR as purely a regulatory must-do, when it actually also suggests simple best practice in data management.

There is an opportunity to pool resources in order to build common solutions in non-competitive functions, and work with trusted third parties leveraging shared data to create actionable client and risk insights. Banks need to work with the regulator to shape policy, coming at the problem from the front foot.

As digital identity (such as SwissID) takes off, this will become critical to managing personal data flows and banks need to be ready for this. Various state-affiliated businesses, financial institutions, insurance and health insurance companies in Switzerland are already working together here through the development of SwissID. This system allows personal data to be exchanged in encrypted form and protected against unauthorised access. The synergies created here mean that partners improve efficiency, reduce costs and lay the foundations for easier data-sharing as a foundation for AI.

Final Thoughts

Bankers in Switzerland have a great opportunity to use AI and analytics to better serve their clients, increase profitability and manage risk. But bankers need to think very differently from the past – thinking about the scale of data available to them, rather than purely the scale of their assets under management, about tailored experiences for their clients rather than mass production, and AI-augmented performance rather than relying on human ingenuity.

To find out more and download the report please visit our website.

Antonio Russo

Antonio Russo - Partner, Service Delivery Transformation, Zurich

Antonio leads Business Transformation in Switzerland. He has over 17 years of experience advising companies across various industries in Shared Services, Business Process Outsourcing, intelligent Automation and Global Business Services and has delivered various large transformation projects.

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Njones

Nathan Jones - Director, Consulting, Zurich

Nathan leads Analytics and Information Management for Switzerland and leads Deloitte’s global community for Information Delivery. Nathan helps his clients modernise their data, analytics and cognitive capabilities so that they can create value from generating and acting on insights driven by data. Nathan is primarily focused on the banking industry and has 20 years’ experience in the data and analytics field.

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Pescud

Elizabeth Pescud - Consultant, Analytics and Information Management, Zurich

Beth is a Consultant in our Analytics and Information Management Team based in Zurich. Her focus is analytics strategy and data governance in the banking and financial services industry. She has over a years’ experience in requirements gathering, use case definition, data sourcing and business case development, and is also involved in developing and running analytics training within the business.

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