Banks are gradually transforming into digital organizations, discarding legacy infrastructure and fitfully adapting to new technologies. Some bankers might still feel resistant to this change, but digital organizations are more agile and adaptable to changing customer behaviors.
Meanwhile, fintech companies are trying to leverage the financial services industry's digital makeover to their advantage. Many of them want to partner with banks to achieve their goals. Such partnerships can help traditional financial institutions learn how to foster more innovative cultures and deliver new products and services quicker.
Building these relationships will help banks on their path of digital transformation, but it won't take them all the way to the finish line. Internal obstacles that are prevalent throughout the industry will have to be overcome. One major obstacle will be poor data quality and management.
Digital organizations heavily rely on making the most of their data to improve their operations, products and services. So institutions must optimize data for analysis and sharing with different teams, partners and applications.
Historically, banks have had trouble with this. For years, they've struggled to make sense of the heaps of data they collect and house in silos with limited visibility across their organizations. To become a truly digital organization, banks must break down these silos and implement new technologies and policies to ensure data is both accurate and actionable.
Outside the U.S., banks and regulators already recognize the need to make data more portable among different providers in the industry. The U.K. Treasury, for instance, is pushing for open banking standard APIs, or application programming interfaces, to foster financial innovation.
In the U.S., several large banks have already started to open up their APIs to outsiders. However, they are still several years behind their counterparts in Europe and Asia in this regard.
Worldwide, banks are also exploring how they can take advantage of distributed ledger technology to share data among industry players more seamlessly.
But before banks can create an open data ecosystem that enables more seamless sharing of data and insights with partners, they need to remove roadblocks that are preventing them from making use of their own data and sharing it among their internal teams.
To improve data integrity, banks should first rethink their data collection processes. Different business lines often have their own data collection practices, which makes it hard to correlate data from different silos. Many banks also heavily rely on manual data collection processes that lead to errors, negatively impacting data integrity.
For banks trying to take advantage of APIs and an open data ecosystem, such errors can grind operations to a halt. Applications that query datasets filled with errors slow down considerably as they have to issue additional requests, which results in an unnecessary lag that frustrates employees using internal applications and alienates customers using external ones.
Banks need to take fuller advantage of technologies for capturing data digitally. The smartphone camera has already changed data capture in check deposit, but it could also be used for obtaining many more digital records from customers. For example, banks have been slow to offer mobile-based account opening even though the smartphone camera can easily pull in data from driver's licenses and other documents. This upgrade can lead to fewer errors and inconsistencies across banks' customer data sets.
Tools like validation APIs can also help banks with their data integrity. These specialized APIs ensure that data inputs and outputs from specific applications are complete and conform to designated formats. Data cleansing and transformation tools can also help correct errors and implement common formats across disparate data sets.
Still, these tools are limited by banks dealing with data coming from many different types of sources. Banks know by now that the number and variety of customer data sources is exploding with the proliferation of new consumer technologies. So to truly build an open data ecosystem, banks need to standardize data collection and management processes across their silos.
Many banks have already begun this work to help rationalize their systems portfolios and to gain a better understanding of their customers. However, standardizing systems and processes across silos that have been built up over decades will take several more years to complete for many institutions. To speed this up, banks should bring different teams together from business lines, IT and operations to create a single group responsible for issuing data standardization directives and setting deadlines for their implementations.
Those directives should align with a broader data management strategy that starts with the organization's business goals. For instance, for a bank focused on increasing its mortgage lending business, the data strategy should start with figuring out what specific data is needed to grow that business and market to new or existing homeowners. Then, the strategy must account for how to acquire or capture that data and store it.
Too often, banks think about building a data strategy by starting with their existing data sets and data capture and storage assets, and then working back toward the business goals. This approach often leaves banks lost because they have too much data to sort out what's actually relevant for the business goals. Instead, they need to narrowly define a set of data needs for the business before sorting through mountains of data.
This approach will help banks deal with their internal data issues. In the long term, however, unlocking the benefits of an open data ecosystem will require industrywide standards. The U.K.'s proposed open banking API is one example of an industrywide standard that could deliver huge benefits in cutting down on systems integration costs and speeding up the delivery of new products and services.
Another example is the potential of distributed ledger technology to settle transactions and contracts involved in securities, trade finance and other areas. Last year, Santander predicted that such use of distributed ledger technology could save the industry $20 billion per year by 2022. However, distributed ledgers require standardized data formats. Transforming internal data to market standard formats will likely be a slow and painstaking task.
Banks that have already started standardizing data collection practices and cleansing data warehouses can adopt such industry standards more quickly though since they will have a more complete view of their data assets and pipeline. Banks that are slow to start this journey will find themselves unable to reap the full benefits of tools like APIs and distributed ledgers, and risk being disrupted by more agile competitors.