How data is driving high-speed customer acquisition in financial services

  • Tomer Guriel, Co-founder & CEO at Ezbob

  • 10.06.2021 07:19 pm

Winning more customers in the financial services industry has never been more challenging. By applying the latest AI technologies and automation techniques to the plethora of data now available, banks and other financial institutions can boost both customer retention and acquisition.

When it comes to financial services, much of the scepticism from the fallout of the financial crisis in 2008 has never really disappeared. In the aftermath of the pandemic, companies are also having to deal with customers’ enhanced digital expectations and pressure from the data-driven big techs, such as Amazon and Google.

As embedded finance takes a firmer hold, financial services firms are also facing new competition from non-traditional financial service providers, who are drawing in customers with fast, convenient, and personalised services.

Against this backdrop, forward-thinking financial services companies are turning to data strategies, specifically how they can harness Big Data to delight their customers and attract new ones. And data has never been bigger – it’s estimated that the Big Data industry will be worth $77 billion by 2023.

Harnessing the power of open data

There’s a wealth of data to draw upon. The banking sector generates an unrivalled quantity of data, with the amount generated each second in the financial industry set to grow by 700% this year. Then, there’s also multiple third-party sources for banks to draw on in the data-rich Open Banking environment.

Banks and other financial services organisations need to harness this data to improve their bottom line, using it to both increase customer retention, which increases the chances of cross-selling, referrals, and ongoing fees, and to optimise the customer acquisition process. In order to stay competitive, they must re-engineer their products and services with an emphasis on enhancing the user journey.

But they can’t do this alone. New problems demand new partnerships. Through a combination of alternative data, such as e-commerce data, and advanced AI algorithms provided by fintechs, banks can get a more accurate picture of credit risk and affordability, enabling them to extend their services to consumers and businesses that truly need them.

Exploring the use cases

Recently, we helped a Tier-1 international bank to reimagine its bank account opening process to significantly enhance it for its sole trader and SME (small and medium enterprise) customers.

The bank’s onboarding process comprised some 90 questions, a length that was negatively impacting conversion rates. We used our range of third-party data sources to better understand what we could automate, and what could be removed from the user journey.

Working together, we reduced the questions down to 28, which has boosted conversion rates. We implemented automation in two ways: via a selfie-based ID process to capture basic personal information, and through Open Banking, which provides accurate, real-time financial data based on the applicant and their firm’s financial behaviour.

In this way, Open Banking is helping financial institutions to conduct a more detailed risk analysis that incorporates traditional credit bureau data, but also multiple other sources to check where the sole trader is registered, their credit history, and so on. Advanced fintech systems can also pull other information that enriches each case – for example an adverse media check on the individual and business, AML screening, sanctions lists, and a device security check.

We also worked with Metro Bank to help them take part in the UK government-backed Bounce Back Loan Scheme (BBLS), introduced to help SMEs cope with unprecedented economic volatility. This was a whole new market for the bank, which hadn’t traditionally participated in the unsecured SME lending space.

Within a month, and amidst huge demand, we created a solution that taps into the Open Banking environment and aggregates information from over 40 different data sources using AI-powered decisioning analytics. This leads to better risk management and more informed credit decisions. It includes the ability to provide loans based on non-traditional online data, such as Amazon, eBay or PayPal activity. Decisions and offers are made within minutes and the money is transferred without any human intervention.

Metro Bank was ultimately able to build a loan portfolio in months that would otherwise have taken many years to achieve, and is now focused on expanding the relationships with these new customers. The benefits for Metro Bank’s customers include enhanced user experience, a quicker journey, and automation. More than 90% of users completed the application in less than five minutes.

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