Nick Pike, RVP U.K. & Northern EMEA SnapLogic, at SnapLogic
The past year has been volatile for the financial sector, with the notable collapse of Silicon Valley Bank, rising interest rates, and concerns about recession.
This is driving a growing awareness of the importance of data – specifically, reliable, real-time data that can be used to provide vital business insights, fuel decision-making, remove inefficiencies, de-risk processes, and help organisations meet regulatory requirements.
In a volatile landscape, good data can be the difference between staying ahead of the curve, and lagging dangerously behind.
What is data integration?
To get the best value from data, it first needs to be integrated. Integration is the middle office of the technology ecosystem. Like the middle office in banking, integration is typically not a high profile, exciting business. Data integration itself is the process of connecting data, whether across differing applications, formats, or otherwise. Unifying data in this way can ultimately make data more useful and valuable for the organisation. As I said, not very sexy!
However, in times of change without agility in the integration layer, the business ceases to function efficiently and in many cases ceases to function entirely. Having a unified approach is then reflected in the deeper insights that can be gained from sales or customer interaction data. These insights can help a high-street bank personalise and upsell products and services to specific customers, based on their behaviours; or to identify any unusual (and possibly fraudulent) activity on an account.
For Pepper Advantage – a global credit intelligence provider – integrating their data following an ambitious global expansion meant they were able to set themselves apart from competitors, producing actionable account insights for both customers and decision-making leaders. This approach has been so successful that the organisation now intends to build data tools for its customers, so that they can interrogate the data themselves and generate insights into issues that impact markets, such as the cost of living crisis.
Not all data integration methods are created equal
While data integration lays the foundations for evidence-based decision-making, diversity in data formats can mean reaching this point is challenging for those focusing on a hand-coded integration approach.
Without an appropriate tool dedicated to integrating data intelligently, businesses often put more labour and effort into what can be, with the right tools, an automated process.
A prime example of this is the FCA-regulated payments business Vitesse. Vitesse struggled to manage the wide variety of file formats that its customers used, each requiring a bespoke input regime. This in turn led to a scarcity in internal resources, as development teams were taken away from day-to-day tasks to work on customer onboarding.
This labour-intensive approach means the time to deliver insights just keeps getting longer and longer, making it a big concern in a world where real-time data is key. So what’s the answer?
The Generative Integration approach
The coming wave of generative AI (GenAI) will mean that the data transport layer in every company will need to change rapidly. Like the spotlight that I experienced, Covid-19 shined a light on supply chains, GenAI will mean a service that until now has been anonymous is about to have its day in the sun. Data integration supports the automation of financial institutions’ processes. Robotic Process Automation (RPA) started the change but GenAI has found new ways to reduce unnecessary human intervention and increase accuracy for a variety of tasks. GenAI is also barely a year old and just getting started.
GenAI is currently inspiring a huge range of developments thanks to its ability to perform actions from natural language input. As a result, it’s transforming how many organisations approach data-related challenges. The meeting of GenAI and data integration has given rise to Generative Integration – a new approach that leverages the power of GenAI to automate many aspects of data integration pipelines.
An early adopter of Generative Integration is Hampshire Trust Bank (HTB). HTB is using SnapLogic’s generative integration platform, to enhance their already sophisticated data integration processes. HTB’s IT team can not only use Generative Integration to speed up automation, but they can also use it to do other time-saving tasks, such as building AI chatbots and automating documentation. Through Generative Integration, data pipelines can be developed, optimised and analysed with conversational language, meaning global institutions have more collaborative ability.
An AI future for data in the finance sector
GenAI is undoubtedly just getting started: 2024 will likely be the tipping point for the technology – the moment when it reaches mass market momentum and any organisation not using it to enhance productivity will, by default, fall behind.
However, any tool must be carefully considered for how it will truly benefit the organisation, and it’s crucial to not forget that using GenAI is just one piece of the data management and integration puzzle. As part of this, a clear data vision is fundamental. Businesses should have a clear idea of what the endpoint of their data journey looks like. Is the priority accessing data to gain insights into business practices? Is it automating manual data tasks across the organisation? In the brave new world of GenAI there are many possibilities and engaging with experts will help you plot your Generative Integration journey.
As part of the consideration approach, there must also be a demonstrable dedication to data security and the ability to adhere to regulations relevant for all jurisdictions the organisation is looking to operate in. This means security must be front of mind and baked into the foundation of any tool used, something that is especially crucial when considering tools which leverage GenAI. The right solution will make sure vulnerable data won’t ever leave its institution’s server.
The final step
Before making a choice for a generative integration solution you need to make sure all concerns have been addressed, and getting support for data transformation is a key part of this. Progress requires a united approach. It’s essential that education for leaders across the organisation takes place. With this, financial institutions are then in a better place to encourage users in all functions to embrace new ways of working.
For those financial institutions that can find the right approach to their data integration challenges, and beneficially leverage GenAI, the future is bright. They will be able to capitalise on clear competitive benefits in a continually turbulent world: boosting productivity, reducing costs, enhancing profitability and developing new customer inspired products and services.
