Financial businesses will always want to base their decisions on accurate data, improve the customer experience, and successfully identify financial risks. That need has become much more pressing with the prospect of AI in financial services, which is something that many businesses are both wary and excited about.
On 21st May 2024, Agile Solutions and Informatica hosted a breakfast roundtable at Claridge’s, London, where data leaders from major British and multinational businesses discussed AI, Master Data Management (MDM), and business transformation, the challenges they have faced, and the opportunities that are in front of them. Here are some of the key points of discussion, lessons, and observations from the morning.
Businesses need a lot more clarity before we see the wide use of AI in financial services
The attendees expressed a lot of uncertainty about what AI will do, how organisations should approach it, and the risks it presents.
One noted that financial services is a risk-averse sector, so any business within it will insist on a lot of due diligence and testing before they are willing to introduce it into their commercial ecosystem. Innovations can offer a first-mover advantage, but, as one participant put it, “We want to be absolutely sure [that AI is safe], because one minor slip-up can have big ramifications.”
Most organisations would prefer to let others go first, and possibly make the mistakes first, so that they can learn the easy way rather than the hard way. Under those conditions, said one guest, “Who wants to be a trailblazer?”
Despite the caution, there is still a large appetite for AI in financial services
Organisations are enthusiastic about AI and harness its potential for efficiency, insight, data & analytics, and customer experience. One participant relayed, “We’re all treading carefully and keep asking ourselves, ‘When can we take the stabilisers off?’”
In many cases, investors are also keen to see innovation towards artificial intelligence, especially when they spot rivals launching AI projects. However, it is not clear exactly what shape that innovation should take, and there are few use cases on which to base AI initiatives.
Attendees recognised that AI could exist ‘behind the scenes’, helping businesses make commercial decisions and streamline operations, but the future also holds customer-facing AI, which could make the customer experience faster and more personalised. Participants were very aware that the stakes are high with consumer-facing AI, with the potential for serious reputational damage if the experience is off-putting or worse, unethical.
High-quality data is fundamental to the success of AI in financial services
The participants were unanimous that data quality would underpin any successful AI initiative, which requires proper Master Data Management and Data Governance. As one guest put it, “Over time it all comes down to the foundations and making sure you've got the right data governance in place. You have to structure data in the right way and manage it properly.”
AI is a ‘multiplier’. Machine learning algorithms and large language models repeat and amplify what they learn. That means if there is poor quality data, AI will produce more, worse data. If there is a bias in the data, the algorithm will take that bias to its logical extreme. If the data is incomplete, then an inaccurate picture will inform your AI-powered data and analytics, and your decisions will be based on incorrect conclusions.
The most common barriers to business data initiatives
The attendees shared some common obstacles to data projects and transformation initiatives.
Buy-in
For a data project to succeed to requires company-wide buy-in. Technology alone is not the solution — software is an enable, but if poor practice and a poor data culture persist, then data transformation will fail.
Part of that is an educational issue, where people are not aware of the impact that Master Data Management and Data Governance could have, or how much impact their attitude to data has on the business.
Budgets
Sometimes budget holders give vocal support to a data transformation initiative, but don’t back it financially. Sometimes they ask that teams take on additional work, or that other projects free up resources, but don’t always hire new talent or invest as required.
It might also be the case that the number of bureaucratic hoops frustrates projects by slowing budget approvals to a standstill.
Architecture
Different teams and departments do not always align on what the ‘data journey’ is or should be for their business data, so they cannot agree on the best architecture to support it.
Compliance
Many business leaders are unsure of their organisation’s obligations with AI, and the best practice surrounding tools like ChatGPT. That makes it harder to create and stick to an AI plan.
Interested in learning more about MDM and its impact on business data and AI?
Agile and Informatica are helping businesses lay the groundwork for AI in their operations, sales, marketing, and customer experience, by helping them to access, integrate, and trust the data that powers a digital transformation.
Across Master Data Management, Data Governance, cloud migration, analytics, and engagement, Agil helps you to implement Informatica’s powerful data tools to ensure better service, faster delivery, and lower costs.
- Here is an Agile Solutions Guide to Customer MDM.
- Watch this video for a summary of how to control customer data with MDM.
- Here are the Five Steps to a Golden Record.
- This is how one credit management company improved the customer experience, extracted extra value from their accounts, and protected vulnerable customers using MDM.
- Here is how to build a business case for MDM in financial services.
Learn More about Informatica’s Master Data Management solution here, and if you would like to hear some expert, strategic advice for implementing a data management solution in your business, building a data culture, and deriving the full value of your data, feel free to contact swhiting@agilesolutions.co.uk.