CHAPTER 02
Opportunities and Best Practice in Modern Data Strategy and Architecture
The improved availability and pervasiveness of customer data creates countless opportunities for the banking and financial services world. The key opportunities revolve around improved customer experience and improved operational efficiency.
For example, an insurance company might sell a car insurance policy to a young person through their parents’ policy. A data-driven architecture that allows data to be integrated from multiple silos means that the customer is recognised, even if their previous interaction was with a different part of the organisation. This means the company can use that insight to offer the customer a new feature or product at the right time, such as a short-term car insurance policy that covers their vehicle abroad, or home insurance for student accommodation.
The first step in building a data-driven architecture is to create a solid data foundation and then, using tools on top of this foundation, to enable improved processes and decision making. Key areas where a data-driven architecture can deliver value for financial services include:
Improved customer experience
Data-driven architecture can support the delivery of tailored products and services based on buyer behaviour, demographics and profiles. Additionally, strong data can be used to support predictive analytics, giving BFSI organisations insight into which customer plans to leave a fund, or when they may next need advice.
In its early stages, data integration can reduce duplication by, for example, giving customers a more seamless experience, by reducing the need to enter data multiple times. At RSA, the company has invested in app technologies that make the consumer journey faster, and simpler, aiming to emulate the seamless consumer experience of Apple or Amazon..
“You can download the RSA app from the App Store, and authenticate with Face ID. Because we have the authentication, we can populate customer data without your needing to complete it twice. We can then give you the best price and provide an electronic document for e-signature in seconds,” says David Germain, Group CIO, RSA Group. “Those are the things we need to do to create a better experience for our customers.”
Increased operational efficiency
Machine learning can be applied to enterprise data to help predict operational demand, based on historical data. For example, data might be used to analyse historical data around customer queries or requests, and support automated responses to future queries. This drives significant benefits in customer service and improved operational efficiency.
Risk mitigation by adequate control frameworks
In some cases, financial services organisations have created data-driven warning predictions that use liability analysis on exposures, prior to default. Combining AI and ML can help to detect financial crime and fraud, allowing organisations to move from rule-based algorithms to machine learning.
Adopting best practice in these organisations will allow these opportunities to be realised. One of the first steps in realising the potential of a data-driven architecture is having the right skills in place to support the transformation. This includes a strong internal team, working with specialist third party partnerships, where needed.
RBS has created a dedicated digital experience team that is charged with mapping customer architecture and experiences, says Priyesh Ranmal, Head of Digital Experience with RBS. “We’ve got a lot of work happening looking at everything from opening an account to getting a lending product. We are doing things in that way so we can find efficiencies and identify new technologies that could help us through that customer journey,” he says.
RBS is also improving integration between in-house and third-party data sources. “We are trying to move into an API way of working, creating connection points so we can integrate with organisations that provide data on our customers,” says Priyesh Ranmal.
Having them API-enabled means we can consume third-party data very quickly and use that to service our customers. Without it, there would be a significant stumbling block, and time to market would increase.
Priyesh Ranmal | Head of Digital Experience with RBS
Working with third-party providers can improve the time to market of new offerings, but ideally banks should look at their own data assets first, says Gerd Pircher, Chief Executive, HSBC Italy. Gerd Pircher argues that banks shouldn’t buy into the idea that they necessarily need third-party data to deliver customer-centricity. “It’s really inertia holding us back because there are very few examples of incumbent banks who have shown that they are really on top of the data they have in-house and are comfortable maximising the commercial use,” he says.
Instead, BFSI organisations should walk before they try to run, Gerd Pircher advises. This means using what you have in-house well, before considering collaborations with third-parties. “One of the key foundations that underpins that inertia is that they are simply not set up and focused around customers so they really struggle to take the data they have in-house, use, manipulate, understand and draw commercial benefit from it.”
In the vast majority of institutions, using data to drive a more customer-centric strategy will involve making changes to legacy systems. Whether you are looking to modernise, replace or integrate legacy apps and systems, the transformation must be driven at board level, says David Germain, Group CIO at RSA Group. “It’s so complex. You want to produce a better customer experience to win more business, but there are revenues associated with what you have, and systems that work today. It’s essential that transformation is driven by someone who can take that broader view.”
Every major BFSI organisation has tried to solve this conundrum and they all face similar challenges, adds David Germain, Group CIO, RSA Group.
Coming off a mainframe monolithic environment is never easy. Think about the thousands of days of effort it takes to enrich functionality. You have to figure out what technologies can improve your customer experience, fast-track products and services to market, make you more efficient within your back office and contact centres. But then you also have to look at how you simplify your heritage environment at the same time, so it complements these new technologies.
Next: Chapter 03
Delivering a Data-Driven Architecture
