Better data integration can facilitate a more equitable distribution of finance

As we look to accelerate full-scale economic recovery after Covid-19, it is imperative that finance is channelled equally to all parts of the UK.

This feeds into the government’s ‘levelling up’ agenda, which aims to redress “geographical inequality” in how opportunities are spread by “improving economic dynamism and innovation to drive growth across the whole country”, thereby potentially adding tens of billions of pounds annually to the GDP.

There are two challenges to overcome. Firstly, equity lending is largely concentrated around London and the south east, where most demand and collateral have historically been. We need to fundamentally change this mind-set, both at policy level and through closer industry collaboration, to ensure that growth is fairly distributed.

Secondly, the ability of organisations to streamline the lending process is hindered by data silos and inefficiencies. This relates to my first point, as a more equitable distribution of capital relies on transforming outdated credit-decisioning models through enhanced data integration.

The industry is digitally fragmented and lacks standardisation. While the pandemic was a catalyst for banks, lenders, insurers, credit rating agencies and others to ramp up their digital capabilities, we need greater integration of data between these entities. By integration I mean aggregating disparate data to provide 360-degree visibility for all parties involved in the lending process. We need to cultivate a financial ecosystem based on tighter connectivity between service providers so that lenders can tap into all necessary information from a single ID.

Overcoming data silos to drive integration and automation

Too often, departments within financial institutions are not communicating effectively with one another, leading to data gaps and cumbersome reconciliation processes. Moving from paper-based to accepting documentation via digital channels does not make mortgage or other lenders digitally enabled, nor does installing a sleek app at the front end. Embracing innovation means eliminating data silos and having well-oiled back office operations for the seamless, automated exchange of information not just within – but also between – organisations. It then becomes easier to employ automation for faster and more accurate decision-making. Open banking went a long way to creating this infrastructure for banking, but it needs to evolve further into ‘Open Finance’.

It also holds the key to more agile credit modelling. At present the model is largely static but as incomes and expenditure vary greatly between regions, the assessment of credit decisions needs to be just as dynamic and tailored. McKinsey estimates that, by adopting new credit models based on harnessing data more effectively, organisations can achieve 20-40 per cent improved efficiency and up to 15 per cent higher revenues, driven by higher rates of acceptance, enhanced customer experience and lower acquisition costs.

Embedding data fabric strategies can enable a bigger range of data sets from new sources to be processed in real time, and the use of machine learning for optimised data management and analytics. This allows organisations to generate in-depth insights from better visibility of data and contribute to an ecosystem that is digitally prepared for connecting with more people than before.

Breaking down access to lending for all regional communities requires the financial services industry to join forces and agree a common set of standards. We have seen encouraging progress already, including a rise in the number of lenders with a regional focus such as Bank North, Birmingham Bank and Cambridge and Counties.

However, established players must also adapt to the current climate to enable the more equitable distribution of finance and bolster growth where it is most needed. Only then can we emerge from the macro headwinds of the past few years as a stronger society and economy.

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