How Data Science Can Save the Traditional Banking Industry

Print Friendly, PDF & Email

Sponsored Post

Brought to you by Dataiku, the makers of the collaborative data science platform Dataiku Data Science Studio

In today’s technologically advancing world, traditional banking groups are being seriously challenged. From big internet giants such as Google, Facebook, and Amazon to small, innovative startups such as Square, CreditKarma, and TransferWire, competition is coming from all sides. What’s more is that the younger Millennial generation is embracing new forms of technology instead of traditional banking. In fact, one-third of the Millennials surveyed don’t think they’ll need a bank at all in five years.

As GAFA (Google, Amazon, Facebook, Apple) giants offer more and more banking services and financial technology startups gain traction, the banking industry must take a look at how it can stay competitive. To do this, banking needs to rely on data science.

Why Data Science Offers Banks a Competitive Edge

Banks have been around much longer than any of the Internet giants or startups, so they have the experience advantage. Banks must leverage these types of advantages and couple them with new technologies in order to stay relevant.

Banks Have So Much Data
While Google is still the data king, banks have historical records and customer information data to pull from dating back decades. Leveraging this data, banks are able to:
• Understand their customers better
• Target new customers based on demographics
• Tailor offerings and create new services based on demand
• Make better, data-driven decisions for the company

Using machine learning, banks can use this data to predict what their customers will want in the future and act accordingly. For example, banks can assess data from a new product or service to predict which demographic will use it. This can be extremely valuable for decision-making in banks.

Banks Can Combine Physical Location and Digital Experience
It’s unlikely that an everyday person can walk into the Google offices and ask a question, but banks do offer this service. By coupling in-person and online experiences, traditional banks can cast a wider net in terms of customer support. Digital-only relationships have limits, and many customers prefer receiving financial advice from a real person instead of someone online. Big data can help banks assess where to open new physical locations and what type of in-person financial assistance to offer customers.

Banks are Trusted
70% of respondents in an IBM survey about disruptive forces in banking noted that they trust traditional banks more than non-bank competitors. Banks are trusted, and can also use big data to improve overall security. Big data and machine learning, for example, can be used for fraud prevention. Using unique algorithms, banks can use machines to detect anomalies in a consumer’s spending. This not only has a zero rate of human error, but it also frees up employees to focus on more high-level security risks. Banks can leverage their level of trust and combine it with proper big data techniques to improve customer satisfaction.

Banks are Ready for Big Data

While the use of big data in banks is slow, that is not because they lack the expertise. The banking industry boasts a deep reservoir of professionals with both extensive domain expertise and advanced quantitative skills. Financial services already have employees with advanced mathematical, analytical, and statistical skills, providing them with a ready workforce of industry-savvy quantitative experts who can be trained to compete with GAFA and fintech.

How Banks Should Use Big Data

There are multiple ways in which banks can utilize big data. From internal checks and balances to better customer service, here are three – amongst many – examples of how banks should use big data:

1. Risk Management
By mining the data that banks already have, banks can better understand market risk for itself and its customers. Furthermore, this risk can be broken down by demographics or geographies, so banks can more fully understand the market. Big data can also be used by credit risk testing for customers, making that process more fluid.

2. Better Meet Consumer Needs
Harnessing the power of big data, banks can analyze a specific consumer’s spending habits, and offer guidance or tools that can improve their entire financial situation. Customers get a more personalized financial forecast and banks can do it all through data analysis.

3. Internal Measurement
Big data can also measure employee and overall business performance, ensuring the bank meets specific goals. Data tools can monitor employee progress and company risk factors. Data tools can measure performance about a new product or feature, comparing it to past product launches to help the team evaluate success.

Banks moving into the Digital Age
Banks that can respond to these new challenges, skillfully exploit their competitive assets, and assemble the right people, data, tools, and processes to get the job done, are likely to succeed.

While this traditional industry may be slower to change, big data can help leverage existing advantages over new players to help improve customer satisfaction and retention. And that is what banks must do in order to prevail over GAFA and startups to become the new marketplace innovators of the 21st century.

Going Further: Surviving and thriving in the era of Internet Giants and Fintech
Banking and insurance companies have some inherent advantages they can exploit to get an edge in big data. Dataiku’s latest free whitepaper serves as a step-by-step guide to helping incumbents and traditional banking/insurance businesses to embrace and implement data science solutions – painlessly.
In the free whitepaper, you’ll find case studies from the industry with real-life examples and applications of data science helping businesses around the world with companies like QxBranch, BlueDME, and many more! Download it now.

Learn More About Dataiku
Dataiku develops Dataiku Data Science Studio, the unique data science platform that enables data teams to collaboratively build and deliver their own data products more efficiently. Over 100 companies such as AXA, L’Oreal, NPR, HostelWorld, or Accor Hotels use Dataiku on a daily basis to collaboratively build predictive dataflows to detect fraud, reduce churn, optimize internal logistics, predict future maintenance issues, and more. Try Dataiku DSS for free.

Speak Your Mind