NEW DECISION MANAGEMENT TECHNOLOGIES HELP MID-TIER AND SMALLER BANKS OVERCOME BIG DATA CHALLENGES

By Ashoke Dutt

Banks have always had to manage large amounts of critical data. Yet, in spite of the buzz around the value of “big data”, most frontline employees in mid-tier and smaller banks still lack direct access to timely and relevant data insights.

Ashoke Dutt
Ashoke Dutt

Traditionally, data analytics has been seen as a back-office function that utilizes expensive,complex business intelligence solutions and requires a level of IT support often out of reach for smaller institutions. But not anymore.

Thanks to innovative, new “decision management technologies”, smaller banks — without large IT staffs — can transform how their less tech-savvy employees work with data and get the same kinds of actionable insights as larger banks.

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As the banking industry becomes increasingly complex, these new cutting edge data technologies help mid-tier and smaller banks mitigate risk and fraudulent activity and create more compliant environments. And, they provide real time metrics to improve customer service and enhance product development — all at a fraction of the cost. 

Struggles With Traditional Data Analytics

Many mid-tier and smaller banks struggle with basic customer and business analytics because they cannot afford traditional data management platforms that are expensive, highly technical, and often in compatible with their existing infrastructure. Built for power users who routinely prep, collate, extract and compile data and reports, these platforms are difficult and time consuming for nontechnical employees — even after training.

Unfortunately, these barriers prevent frontline employees and their managers from having direct access to information that is critical to timely business decisions and customer response.  Prompt responses to regulators is also of particular importance to smaller banks since they are often subject to as much, if not more, regulatory scrutiny than larger banks.

New decision management solution stake aim at all of these challenges. Most are highly “self-service” in nature, low maintenance and do not require expensive technical consultants to get work done. They empower smaller players to be more nimble, competitive and satisfy their employees’ growing need to access information. 

How It Works: Conversations with Data

Today’s decision management platforms use technologies like artificial intelligence, machine learning and cognitive search to perform simple vocabulary-based data analysis. What this means is that non-technical users can perform their own analytics by simply typing questions like “How many customers with more than one account take advantage of special perks for having multiple accounts?” into a familiar, Google-like search bar. Within seconds (not minutes!) the platform responds by generating a customized report of real time, authenticated data in easy-to-understand formats.

Business users in every corner of the organization can become their own data analyst, engaging and collaborating in real time data exploration to investigate business hunches, increase efficiencies, share insights and information,maximize returns and minimize risk exposures. Here are just a few ways teams can benefit:

  • Governance, Risk and Compliance can use AI, machine learning and cognitive search to automate the process of searching, understanding and managing changes to regulations. A simple search-like interface can be used for everything from looking for policy and procedure documents to complex analysis of transactions by searching across both structured and unstructured data.
  • Risk Management can use natural language querying and machine learning to connect the dots between emails, phone calls, text and transactional information to spot anomalies or compliance issues. (e.g. bankers opening multiple accounts for the same customer; creating online banking accounts with bogus email addresses; customers performing fraudulent activities). Combined with predictive analysis, this functionality can help predict likely occurrences of fraud as well as detect and report of fraudulent patterns.
  • Sales & Marketing can easily track sales performance and other key performance indicators (KPI) using both internal and external customer data. This improves their understanding of customer behavior and how to refine customer segmentation.
  • Customer Service now has the ability to search across multiple data repositories and easily extract customer data without using multiple systems and windows. This facilitates response time and accuracy, improving customer satisfaction and outcomes.

Considering the latest advancements in data analytics, there is no reason why mid-tier banks still need to rely on yesterday’s expensive, complex technologies and deny their business users access to the kinds of actionable data insights that benefit larger banks. 

Ready to take the Plunge? Avoid the Pitfalls

In order to insure your organization gets the long term value it deserves from a decision management solution, make data analytics a part of your corporate DNA. This means getting top management involved as well as line-of-business managers.

Let’s take a look at some basic things to keep in mind:

  • Do your homework: With a growing number of solutions available, look for a platform that is adaptable, scalable and customizable to your unique business needs. The solution should be truly “self-serviceand require minimal employee training and vendor support to run and maintain. Take the time to properly define your business requirements upfront so potential vendors can help you make the appropriate decisions.
  • Get your workplace onboard: Remove corporate resistance to empowering frontline staff to access data. Executive Management should support a workplace culture free of central gate keepers who decide how, what and when information should be fed to the business.
  • Reduce the need for human intervention: Think twice about investing in multiple incompatible tools and building a larger IT team to manage disparate systems. This is a decision that many large banks suffer from today and smaller banks should steer clear of this strategy.
  • Find a technology partner that meets your needs: Consider doing a gap analysis on the needs and support required by your business teams to achieve company objectives. Ask potential technology vendors to address these gaps and do not settle for rigid prescriptive tools that only partially get the job done. The right technology vendor must be a good cultural fit for your company and understand what your organization requires to truly meet the needs of its customers, employees, and business goals.

Data has always played a critical role in day-to-day bank operations and solving business problems. Until recently, most mid-tier and smaller bank employees have missed out on the benefits of direct and prompt access to big data. But now, thanks to the growing and evolving world of data technologies, smaller financial institutions finally have the opportunity to level the playing field.

About the Author: 

Ashoke Dutt is the CEO of Semantify, a pioneering semantic search technology platform company based in Chicago, Illinois. His career in global financial services spans more than 30 years and includes the launch of India’s first credit card business in 1989 at Citibank. Dutt went on to assume various other leadership roles at Citibank, including EVP, International Cards. He also served at Morgan Stanley (EVP, International Retail), and Discover Card (EVP, Marketing). Today, Dutt is an active entrepreneur and investor, serving as on boards of various start-ups and philanthropic organizations.

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