Data integration: helping banking expect the unexpected

By Gaurav Chawla
Gaurav Chawla discusses the effectiveness of data integration and its potential for predictive analytics in the banking sector

From the 2008 financial crisis to natural disasters and the covid-19 pandemic, the financial services sector is no stranger to crises. Yet, while these events without a doubt presented significant challenges at the time, they have also taught valuable lessons – namely how to better prepare for the unknown and cope with any future uncertainty. 

One of the fundamental teachings of these crises has been the need to increase resilience through enhanced and improved data infrastructure. The pandemic in particular has highlighted just how vital it is for firms to adapt and improve data management, with the focus firmly on integration and permission accessibility. 

A different type of disruption

However, despite being front of mind right now, the pandemic isn’t and will not be the only challenge banks have to contend with. Disruptive events and sudden changes in trade flows are more common than many institutions like to remember. In fact, market bubbles and continuing tensions between major trading countries have the potential to overturn “normality” at any time. 

On top of this, fintechs and neobanks represent a more sustained threat. These organisations have the potential to eat away at market share, especially in retail banking, where slick interfaces, ease of use, new products and lower costs are attractive to consumers and businesses alike.

For established financial institutions success will come down to how well and how quickly they respond to these sudden crises, while at the same time pushing forward more incremental change to out-compete or forge new collaborations with the fintechs. 

The role of data management 

More advanced data management will be at the heart of overcoming these challenges, especially in the large number of institutions that still struggle with complex legacy systems. Whether it is adapting to new crises, adopting or creating new applications, or building interfaces with partners, financial institutions must harmonise the masses of data they need. To achieve this, combining external, unstructured data in various formats, with the data from banks’ own diverse systems is a key requirement. In a crisis, this needs to happen at speed. We need only look at events such as the 2008 collapse of Lehman Brothers and the blockage of the Suez Canal earlier this year to see just how sudden a crisis can materialise and the significant financial repercussions it can bring with it. In the face of these drastic and unexpected events, leadership teams need fast access to an overarching view of their business and the ability to have accurate and detailed scenarios ready that give them a natural advantage when planning the next move. 

This demands integration of different data layers in a single platform with applications on top of the vertical stack. The new architecture must be simple to provide the required level of agility to adapt to sudden changes. 

In the age of open banking, this approach will also enable established financial institutions to facilitate partnerships with neobanks and fintechs. For these partnerships to work, the fintechs need access to the wealth of data about customer banking and financial behaviour. But that data must be usable and interoperable, or the partnership will never meet its objective. 

Capitalising on the opportunities of data harmonisation

As financial institutions turn their attention to future proofing, it requires their systems to make data from all necessary sources available on demand and in a consistent and accurate format. This is the essential platform on which to build new capabilities that increase agility and provide new services to clients and customers, using advanced analytics, machine learning (ML) and application programming interfaces (APIs). New capabilities provide actionable insights that transform efficiency and increase resilience.

With access to fully harmonised data, business managers benefit from analytics and visualisations that quickly give them a deeper understanding of what is happening in the organisation. While an integration layer normalises the underlying data, ML enables dynamic queries and data analytics, along with API management capabilities. 

Consistent, harmonised access to disparate data coupled with a range of analytical capabilities enable banks to assess and address a large number of risks, emerging threats or critical business initiatives. They have access to near real-time enterprise risk management, liquidity management, the ability to act on market signals, or to boost capital efficiency through faster and more accurate analysis of threats and opportunities. 

Harmonised data is also the key that unlocks advances in artificial intelligence (AI) and ML that have the potential to create better, streamlined user experiences for clients and customers. Getting the data in shape for these technologies is a fundamental requirement and makes implementation less of a chasm to cross, especially if ML capabilities are integrated into the harmonisation platform that brings all the data layers together. 

In the competitive financial services landscape, the ability to integrate data from across extremely diverse systems will be critical for organisations to respond fast and effectively to major political, health or weather events, and embark on new open banking partnerships. Armed with fully and effectively integrated data, banks will be better placed to obtain much-needed resilience and agility, future-proofing them and enabling them to seize significant new opportunities.


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