We are increasingly observing banks focusing on integrating AI into their operations and business priorities. There are budgets being set out to deploy AI in individual functions, but banks are struggling to either prioritise use-cases due to conflicting interests or deploy in an efficient manner with required guardrails. As we work with banks and external FinTechs, we can see use-cases where banks can now derive value with low investment, and simultaneously consider their long-term transformation strategy to maintain a competitive edge within the market.
Based on our analysis of current market challenges and our insights, we have identified more than 150 use-cases which would benefit investment banks across all functions.
Join us as we present a monthly blog series, where we will explore the viable use-cases we are observing in the market. Each month, we will focus on a specific division, including Front Office, Middle Office and Back Office and support functions such as Risk and Compliance.
Please also see our “Artificial Intelligence in Investment Banks” white paper here.
Front Office
The Front Office is required to continuously evolve in response to changing client demands, new regulation and the emergence of new technologies including Generative AI. Banks face multiple hurdles and pain-points to be agile in such a landscape including:
- Reactive coverage to high value clients
- Higher economies of scale to enter new market
- Decreasing cost and benefit of vendor solutions
- Fragmented services and systems with little interoperability or due to legacy systems
In order to transition towards a more client-centric model and provide more cost-effective services, banks are now looking at AI to optimise their sales and trading business to become more data-centric and focus on innovation as a differentiating factor. This shift is crucial for retaining and ultimately increasing traction with key clients. Consequently, banks are now considering:
- How will they enhance the customer experience to ensure they cement themselves as the primary contact for their clients?
- How to serve clients most effectively?
- Which existing or new products and internal capabilities can support our strategy objectives?
- How to best optimise sales and trader performance to get further alpha generation?
How can AI help?
Banks are now testing and deploying AI models that are contributing in addressing the aforementioned challenges. AI can consume a large amount of structured and unstructured data in real-time and thus facilitate in proactive insights which can be embedded in sales-trader workflow. Some real-world flavour that we are seeing increasingly used are:
Use-case examples
- Streamline intake of client information and capitalise on automated data gathering to simplify the onboarding experience.
- Map important terminology from legal documents (ISDA, F&O, CSA, CMSLA etc.) for faster client on-boarding and define credit and margin terms.
- Use of FIX digital twin to test and on-board clients on to single-dealer platform.
Use-case examples
- Gain insights and analytics into client and competitor behaviour, all in real-time, in order to optimise pricing and trading strategies.
- Enable improved data-driven decision making capabilities to sales and trading based on e.g. client behaviours, trading patterns, risk appetite and micro/macro research.
- Personalised client interactions based on historic engagements and market sentiment.
Use-case examples
- Analyse the behaviour of the high performing Salespeople to unlock and uplift revenue through up-skilling Sales and account management workforce.
- Sentiment analysis on sales and trader chat to detect fatigue and ensure well-being.
- Facilitate risk management of rules-checks on individual trader mandates such as risk limits, suitability, margin controls and/or jurisdictional permissions.
How KPMG can help you
We are closely collaborating with Tier 1 and Tier 2 Investment Banks , focusing on AI Strategy and Governance. We are leveraging our use-case library to help banks prioritise AI change initiatives.
Within the front office, specifically, we sit with relevant stakeholders and agree the focus areas and agree on a set of use-cases. We have now started working on several Proof of Concepts / Value (PoCs/PoV) working either with our KPMG Lighthouse team (Data, AI & emerging technologies) or with our carefully curated and specialised Alliance and FinTechs partners to advise, build and support productionising of use-cases. The use-cases are focusing on:
- Defining a comprehensive view of client attributes and real-time insights enable “intelligent” and proactive insights to Sales and Trading teams
- Providing market insights and trends related to peer banks
- Providing a personalised service model, tailored to shape and improve the client experience
Here at KPMG, we offer a wide range of solutions to support banks in their end-to-end journey to deploy AI models from strategy, talent impact, risk assessment, use-case prioritisation to implementation. If you would like to discuss what it means for you, please get in touch.
