By Henry Tam, Senior Solutions Marketing Manager, Redis

The Financial Services (FS) industry has seen rapid changes in recent years, with new customer behaviour driving wholesale changes to operations at all levels. Local physical branches are slowly being phased out, with mobile banking becoming the preferred choice for individuals––especially younger consumers––and transactions are now expected to be processed in real-time. This all poses challenges for traditional banks, and many are relying on outdated legacy systems which simply cannot provide the experiences customers are after.

The modern banking customer expects high standards, and for good reason. New competitors have taken the market by storm, and their flexible architectures and modernised approaches mean they’re able to provide a real-time, omnichannel experience, as well as bring frequent new offerings to their customers. As well as this, banks also face additional stress from new government legislation that demands a modernised data layer to improve the customer experience and offer them extra security. This demand for a consistent banking experience has put added pressure on traditional banks, with their rigid data architecture simply not up to the task in most cases.

If traditional FS firms wish to stay competitive and continue to provide the customer experience necessary for survival, they need to put a strategy in place now.

How can organisations embrace modernisation and change?

Data modernisation is the process of moving siloed data from legacy databases to modern cloud-based databases. This process, while coming with technology, people and process costs, in the end allows organisations to be agile, and eliminate inefficiencies, bottlenecks, and unnecessary complexities that surround legacy systems. A modernised data platform provides efficient data migration, speedy ingestion, scalable, multi-structure model support, while offering flexible deployment choices and optimised storage efficiency.

Modernising your financial data layer doesn’t need to be complicated, nor disruptive. Data layer modernisation follows a chronological process that begins with identifying data accessibility gaps and ends with operationalising the data layer with microservices.

So how do FS organisations modernise their data layer without causing disruption to services? There are six optimal steps that any business can follow as a start:

• Identify data accessibility gaps: Discovering areas where quick access to customer data is inhibited.
• Achieve a consistent omnichannel customer experience: Ensuring the four fundamental expectations associated with a consistent real-time omnichannel experience are met.
• Information models for real-time processing: Maintaining accessibility and interoperability with different information models that support real-time processes.
• Adopting a modern computing service: Understanding the architectural differences and how to transition from developing monolithic systems.
• Embrace data platforms for low-latency access: Discovering the key characteristics of an elite real-time data platform with low-latency access as well as knowing how to leverage it in your architecture.
• Incorporate an operational data layer: Uncovering how to operationalise the data layer with microservices and how to provide rapid access to those systems.

Four use cases ripe for modernisation

Many banks need to modernise their data layer to overcome the myriad of challenges in today’s fast-paced digital environment. Some of the world’s leading banks have recognised IT legacy systems as performance inhibitors and have modernised their data layers  to overcome the biggest obstacles standing in their way.

1.       Supercharging the customer experience

Modernising data architectures means FS customers can enjoy an omnichannel experience that allows them to seamlessly access a range of different digital products and services in real-time, regardless of their location, bank accounts, mortgages, loans, investments, and much more.

2.       Maximising identity management systems

Data sharing with third parties is mandatory for providing customers access to innovative digital solutions that augment the user experience. But doing so exposes financial institutions to the possibility of cybercriminals infiltrating their data.

With a modern data architecture, organisations shield customer information from cyber-criminals while sharing data with third-party organisations. This involves improving the developer experience with granular access to APIs, data, and resources.

3.       Boosting manual investigation processes

 When done traditionally, manual investigations can take up a large amount of man-power and time. Using modern technology allows organisations to sift through reams of data with hyper-efficiency, allowing them to identify any suspicious patterns that would indicate fraudulent activity.

4.       Clamping down on fraud with real-time data

It only takes a few seconds for fraudsters to commit online theft. Therefore, fraud detection is entirely dependent on the speed at which a bank can identify and react to suspicious data patterns. The ability to guarantee real-time data enables large banks to swiftly dispel fraudulent activity through real-time digital identities, artificial intelligence models, and more.

If traditional FS organisations wish to survive in the modern era and avoid being left behind by new, more digitally-native competitors, they need to act swiftly. Outside pressure isn’t going away, and customer demands will only continue to rise in line with new technology developments. Making a decision to follow the steps above and invest in modernising the data layer will ensure that traditional banks can keep pace whilst still offering an interactive customer experience, enhanced risk management, and advanced analytics to prevent fraud.

About Author:

Henry Tam is the Senior Solutions Marketing Manager at Redis where he drives the vertical industry solutions go-to-market strategy and messaging. He has over 20+ years in the IT industry with broad technical and business experience in developing, launching, and managing software solutions that meet the digital transformation needs of data driven industries including financial services, retail, and healthcare.