Banks’ Poor Use of Data Pushes Business Customers to Fintech

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In the wake of what happened with personal banking services, financial institutions are seeing fintechs and other non-traditional players increasingly gnawing at their small and medium-sized business (SME) customers. One of the main reasons for this is the inability of banks to consistently use day-to-day data for tailored customer offers and decisions, and not to take advantage of all the internal and external data available to them.

That’s the conclusion drawn by a new report from Accenture. And that explains why SMEs are an area where fintechs have been very successful in snatching customers from banks and credit unions. Indeed, there are now more than 140 fintech startups serving SMEs and entrepreneurs, meeting needs such as accounting, expense tracking, insurance, invoicing, payment processing and payroll, according to a report. study 2020 11: FS.

The study found that 62% of SMEs do not believe their business bank account offers any additional benefits over their personal accounts. Just over two-thirds (67%) use one of the six fintech trading platforms, compared to 51% using one of the big five banks.

( Dig deeper: Fintech threatens to disrupt small business banking market)

The new competition:

Shopify, which is used by about 30% of all US ecommerce sites, is now the 10th largest platform providing financial services to SMEs.

The Accenture report highlights several notable examples of financial technology disruption in this area. One is Shopify, which is used by about 30% of all US ecommerce sites. It is now the tenth largest platform providing financial services to SMEs. Another is Stripe, which has created an end-to-end lending application programming interface (API) for e-commerce sites to offer financing options to SMB customers, adding further potential for erosion. in the activities of traditional institutions.

“Numerous [financial institutions] respond to these challenges by increasing their investments in data, advanced analytics and artificial intelligence (AI), ”says Accenture. “Yet most have encountered obstacles in their journey towards data-driven reinvention.

“Much of the rich, real-time transactional data that banks have access to is lying fallow. “

– Accent

“While many [institutions] have developed pockets of data and analytics excellence, ”the consulting firm continues,“ they are struggling to scale deployments, integrate data-driven decision making into day-to-day operations and use the data to drive truly transformative change across the enterprise. Much of the rich real-time transactional data they have access to is lying fallow. “

( Read more: How to Succeed in the Banking Services and Small Business Lending Market in 2021)

Tons of data, but key details missing

A major problem for traditional commercial banking providers is that some of the traditional data sources they use to make decisions do not provide enough detail about customer behavior to glean meaningful insight, observes Jared Rorrer, managing director. and Head of Accenture’s Commercial Banking business group. Things change dynamically, and there is a need to react quickly, with the right ideas for decision-making, at the right time, he says.

“It requires an expanded data ecosystem, combining first-party, third-party and broader digital signals to inform the bank’s actions,” says Rorrer. “However, many banks are unable to fully harness the power of their large internal data sets because they do not have a scalable and agile database and the appropriate levels of multi-speed data governance. .

“Banks also face the challenge of understanding exactly what questions to answer and what sets of information and information are best suited to get that answer,” Rorrer continues. “What is important to know about the customer so that the bank can offer them relevant messages and offers? “

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What are the biggest obstacles?

The four main obstacles that banks face in achieving this level of data maturity as described by Rorrer are:

  • Internal silos
  • Focus on incremental rather than comprehensive improvements
  • The “fatigue” of digital transformation
  • Lack of ownership of the business.

Some of these issues are not new. Organizational silos are often cited as a major obstacle to achieving any kind of meaningful digital transformation in banking organizations. For example, Accenture says that 50% of financial institutions cite difficulty accessing data from disparate internal sources as one of the top three challenges in implementing their AI strategy.

When engaging in data-driven transformation projects, financial institutions tend to focus solely on “mitigating immediate problems,” the Accenture report notes, which is more of a band-aid strategy than a band-aid strategy. a global change. This is exacerbated by the time IT departments spend getting banks out of technical debt as a result of years of digital transformation projects, leading to a lack of internal appetite to take on longer IT projects.

These can be strong headwinds to overcome, notes Accenture: “Many find themselves trapped in a frustrating proof-of-concept loop and are unable to expand their data and analytics efforts beyond a few small centers. Excellency. Rather than using data to drive innovation and create new business models, they reap modest returns on investment through low revenue or cost reduction gains. “

How to accumulate data gains

Jared Rorrer notes that banks and credit unions can invest in critical building blocks to break internal silos and make better use of data, such as data access, quality, and cloud-based foundations.

He also says that data-driven institutions invest in empowering employees to perform basic data, business intelligence and analytics tasks. “Banks looking to get the most from their data can mobilize cross-functional teams (business, IT, data and analytics) that work together to share information, fostering a sense of engagement in the process from the start. “

Data managers at financial institutions can gain internal buy-in to larger projects by focusing on small wins first and building up from there. For example, Accenture notes that a bank or credit union may start by using machine learning to inform credit decisions about short-term products where the risk of loss is low. It can then extend these capabilities to other risky products such as overdrafts, corporate credit cards, and merchant cards.

As it grows its data capabilities and confidence, the institution can expand its autonomous capabilities to trade commodities and core lending products such as foreign exchange and derivatives, lending from traditional cash and asset loans.

Ultimately, Rorrer says traditional institutions can begin to win back SMB customers through proactive and preventative customer engagement and by delivering goal-oriented, forward-looking products and services.

Over time, the analyst believes banking providers can increase their efficiency by leveraging data-driven operating models, AI and cloud-based architectures, and strong ecosystem partnerships to foster large-scale transformation.


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