Five Ways Traditional Banks Can Survive and Thrive in the Age of Algorithms

By Florian Douetteau

Today, the banking industry faces rising competition with tech giants such as Google and Apple entering the space as well as a slew of fintech startups and the growing prevalence of the Internet of Things — or, IoT. These challengers bring new, innovative products tailor-made for the connected and mobile world into which they were born.

Tech giants and startups are also bringing new strategies to the banking industry that haven’t been widely leveraged yet by traditional institutions: big data and data science, the collaborative disciplines enabling the age of algorithms. Specifically, data science is the combination of people, data, tools, and processes used to transform raw data into actionable insights and business innovation.

For example, data science is enabling these newcomers to leverage data mining and predictive modeling to personalize offers, reduce risk, create disruptive new products, expand markets, minimize operating expenses, automate traditionally manual processes, and much more. Traditional banks have been slower to adapt in this space, but the battle is not lost – they can still leverage data science to compete and emerge victorious in the internet era.

Here are five ways banks can propel themselves forward:

1. Leverage Unique Assets

Though tech giants and startups have an edge in big data and algorithms, banks have unique assets the challengers lack that, all other things equal, provide advantages and may win the battle for traditional banking:

● Deep, historical troves of untapped customer data. Banks are uniquely positioned to get started with data science quickly since the data already exists and it’s just a question of access and merging.

● Physical touchpoints (branches). This differentiator will allow banks to use data science as well as develop meaningful customer relationships.

● High levels of consumer trust. The traditional banking industry is more deeply regulated and under more scrutiny than startups in the same industry. All else equal, consumers have incentive to choose banks over their challengers.

● Professionals with extensive domain expertise and advanced quantitative skills. Banks are also well poised to assemble skilled data science teams and data labs due to existing staff.

2. Partner With, Acquire and Invest in Fintech Companies

Investment capital for fintech companies reached $22.3 billion in 2015, an increase of 75 percent over 2014. More than ever before, fintechs are powering into a broad range of banking services, including virtual banking, personal/small business lending, financial advising, investment brokering, credit scoring, currency trading, money transfers, equity crowdfunding, payment processing, and more.

These services disrupt and challenge traditional banks, but some have countered by partnering with fintech companies. Others have either acquired or invested in the newcomers; banking institutions have accounted for a large percentage of total fintech investment capital since 2010.

3. Embrace Transformation by the IoT Revolution

In IoT, billions of sensors, computer processors, and communication devices are attached to every kind of ordinary thing imaginable. The types of data collected and uses for that data are diverse, but the general role of IoT is remote monitoring, analysis, and control. For banking, this could mean revolutionary new products based on IoT like the provisioning of loans, leases or purchases based on equipment usage data. Thinking about and innovating into this space is crucial for the financial industry’s survival.

4. Use Unconventional Data

In big data, it’s not just transactional datasets that businesses use for analysis and predictive modeling. Unconventional sources of data, like social media activity or cell phone use, augments transactional data for a more complete customer picture. Combining datasets allows banks to more quickly and accurately do things like assess identity, fraud risk, creditworthiness, and to automate underwriting and loan origination processes.

Startups like Lending Club, Kabbage, LendUp, Affirm, and ZestFinance all already use this kind of data to deliver innovative and personalized customer experiences and better financial performance. On top of these efficiencies, diverse big data combined with advanced algorithms can help banks with expansion, specifically into young or underserved populations with little to no credit histories (a global population estimated at more than 2.5 billion). For example, the startup InVenture has found success providing lending services to people in Africa based on historical, unconventional data sources.

5. Optimize with Machine Learning

Machine learning is perfectly positioned to provide efficiencies in banking by automating traditionally paper-based or manual processes. Algorithms can automate much of these processes and minimize (or possibly eliminate) the need for human review.


NOTE: Author Florian Douetteau is CEO and co-founder of Dataiku, the software developer behind Data Science Studio (DSS), a unique advanced analytics software solution that enables companies to build and deliver their own data products more efficiently.  Companies from Fortune 500’s to small businesses in industries ranging from banking, to e-commerce, to industrial factories, to finance, to insurance, to healthcare, and pharmaceuticals use DSS on a daily basis to collaboratively build predictive dataflows to detect fraud, reduce churn, improve internal logistics, predict future maintenance issues, and more.

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