Open Banking Competitive Edge Through Data Architecture

The banking system is changing. Rather than control everything themselves, financial service providers are looking at building a more horizontal system under the umbrella of a banking network. 

Banks, insurance agencies, loan providers, and so on are looking at ways to integrate their services with other providers. It’s the magic of open banking — a system where banks use APIs to provide third-party developers and service providers with automated, secure access to customer data so they can offer tailored solutions. This modularization allows them to focus on providing specialized services while still giving customers a connected experience — a win-win for everyone.

Customers, too, are willing to have their data shared with other service providers in return for tailored services. For example, personal finance management APIs can pull information from multiple accounts and place payments in different categories to give customers a clearer view of their expenses and investment opportunities. 

To achieve this trust, banks need to stay connected with all service providers and yet maintain data security and comply with regulations. What it implies is that they need to prioritize their data architecture.

The Need for Data Architecture in Open Banking

Unlocking information silos and making data accessible to partners in an open banking system has many advantages but it also has its challenges. Data can be a double-edged sword. When managed correctly, data can help organizations action out their business strategies. But, if not stored and shared carefully, it has the potential for financial as well as reputational losses. 

If data is used for purposes other than its intended use, the consequences could be damaging for banks and their customers.

For example, good data architecture and governance helps organizations comply with AML and KYC regulations but poor control over data can lead to a loss in customer trust and increased risk of financial fraud. Similarly, investing in data architecture can create a more well-rounded, complete customer profile, but failure to do so can lead to wrong decisions that lower ROI. 

Sourcing incomplete or incorrect data from operational systems could cause unanticipated delays and increase costs. The platform could also be exposed to hackers and security breaches. The only way to get past this is by recognizing the challenges ahead and putting together a proactive plan to cross them. 

Data Architecture for Open Banking

With data being accessed by multiple agencies, a clear set of policies needs to be put in place about how this data is to be sourced, assessed, transported, stored, and queried. This is what keeps data agile yet secure. 

For easy understanding, let’s break down the journey of data architecture for open banking into four stages: data discovery, data quality, data sharing, and data retention.

Data Discovery

The first step toward building robust data architecture is structuring data discovery and sourcing. Begin by understanding the legacy data landscape in the organization. Banks need to identify data sets that are relevant to customers and acquire relevant data sets. This then needs to be catalogued for quality and completeness. The data lineage also needs to be mapped to identify relationships and weak spots and take proactive steps to strengthen them. In addition, they need to ensure that data is collected with the customer’s consent not just to storing the data but also to sharing it. 

Data Quality

The success of open banking depends quite highly on the quality of data being shared. Data quality standards establish how fit the data is for use. Before the data is shared by anyone else, it must meet data quality standards and be assessed as correct, complete, valid, formatted consistently, and unique. Being able to identify and resolve gaps and correcting inconsistencies at an early stage reduces the impact it can have. 

Data verification and enhancement tools can play an important role here in running continual data quality assessments over all data sets. For example, these tools can help deduplicate data and merge data from records to enhance the data quality and complete records. 

Data Sharing

Now that data has been qualified as good quality data, the modes of transportation and sharing must be secured. Secure data architecture delivers the mechanics to master and consolidate data requests from customers, partners, and third-party vendors. Data is encrypted as it travels over the shared network so that it cannot be accessed by anyone other than the intended recipient. 

Data must also be controlled so that only authorized people can access it. Anyone who wishes to access the data set must be accredited by a risk-based system to maintain privacy safeguards. 

Data Retention

One of the key aspects of data governance to ensure compliance with government regulations is ensuring data is stored only for as long as the organization has the customer’s permission to do so. Defining steps for data retention, decommissioning, and disposal ensure that all conditions of consent from customers are adhered to. As soon as the customer withdraws consent for storing or sharing data, the data must be deleted and de-identified.  The policies for data retention and disposal are also critical for ensuring that outdated data isn’t stored in the database. 

For example, an organization providing personal loans may need to dispose of a customer’s data when the loan account is closed. Similarly, if a customer updates her address, the old records must be purged from the system. 

Getting Started With Data Architecture

To maximize advantages, organizations must approach data architecture for open banking in a way to comply with data standards and regulations, gain insights that enable the responses required to tailor products and services to unique customer needs, and attract new customers within the existing markets. It is important to find synergies across the umbrella of services so as to achieve the optimal return on investment. 

Open banking is only the beginning of the open data regime. Other industries will soon follow suit to be able to experience the benefits of this system. As pioneers, this is the sector that will set the trend for everyone to follow. 

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