As of the end of 2020, 24.7 million people worldwide use open banking. The estimated growth rate is at a whopping 50% between 2020 to 2024. Open banking is an innovative take on financial services and other related operations, making it a valuable segment as a banking service.
Despite the various benefits offered, the adoption rate by consumers is relatively low. A survey by Axway found that over 47% of consumers were concerned that they would lose control of their financial data under the open banking system. Are these concerns legitimate? How does open banking work? Is there a solution to these concerns?
The traditional banking systems that we are familiar with involve time-consuming paperwork, numerous management sign-offs, and costly teams to develop and test new products and services. Open banking is the gateway to innovation in the banking sector.
Banks use APIs shared with registered third-party service providers, financial institutions, and smaller fintech for efficiency in their operations. So, open banking is a term used for the process of releasing, or “opening up,” data for these regulated providers to use, share and analyze consumer behaviors.
Open banking is safe as the banks have secure infrastructures in place to preserve and share their clients’ data as long as the customer chooses to be a part of the open banking system.
The information shared when a consumer provides consent can be broadly classified into three categories.
This includes information used to identify a user’s account. Such as:
Product data describes the specific products and services offered by a financial institution. Under the traditional banking system, consumers had to visit a bank branch to find such information. As the internet and telecom evolved, customers can now contact the bank or find the details on their website.
With open banking, consumers can find the best options available for them, thanks to the analysis of their financial data. In addition, the information is stored in a standard format, which is easier for third-party providers to process and identify the best products and services for them.
Payment initiations are about transferring funds from one bank account to another. Commonly used by consumers for online payment portals, this process often involves several steps such as inputting the account information, providing a security code, and entering an OTP for verification.
Open banking quickens this process by initiating payments securely through other software, apps, or websites with the consumer's consent.
Open banking relies on APIs that provide third parties with access to the information stored by the bank. The players involved in the open banking initiative include the government, regulators, and banks. These players will need to agree on which APIs are to be used for the open banking system.
The bank can then build upon them and implement them for the convenience of their customers. Small businesses, enterprises, and individual consumers benefit from this application. Some examples of use cases in open banking are:
Accessible Borrowing: In most cases, credit histories can often be leveraged to design unfavorable borrowing terms. However, a customer’s creditworthiness is easily determined through the historical bank account records that lenders can access with open banking.
Account Aggregation: Customers no longer need to switch between multiple accounts or repeatedly log in to engage in transactions. Open banking lets customers view all the accounts under their names in a single place.
Open banking depends on consumers’ consent to participate in such an initiative. However, a survey conducted in 2020 found that only 30% of consumers in Europe were genuinely comfortable with their data being shared even after giving their consent. The show of low faith in the open banking system is due to the lack of transparency by banks in their consent management systems.
Financial institutions, fintech, and data aggregators are accountable to different regulators. This difference can often cause friction between the parties when enabling a transparent and secure data-sharing environment.
Consumers are more comfortable submitting bank statements when applying for mortgage loans. Under such circumstances, it is often a norm as bank statements are a requirement for various commitments.
The same bank statements also provide the account information details consumers are hesitant to consent to under the open banking initiative. Thus, open banking would receive higher consent rates if they requested bank statements over the additional data from customers.
Digilytics’ AI technology combines computer vision, Deep Learning, Natural Language Processing (NLP), and one-shot machine learning. This facilitates first-time-right mortgage loan applications for interested borrowers. In addition, the software provides an incredible advantage to lenders as manual intervention is minimal, resulting in quicker loan application processing times.
As mentioned above customers are more comfortable submitting bank statements to the lenders when applying for mortgage loans.
Digilytics RevEL works on these bank statements by extracting account information such as name, pay stubs, account number etc. The information extracted is then available on an intelligent dashboard.
RevEL provides detailed solutions based on the categorized data and actionable insights. You can access this through APIs or directly through the powerful dashboard.
The actionable insights are developed through the Intelligent Affordability Service of RevEL, allowing lenders with valuable transaction data and information. This is then used to design predictive models on the RevEL application. In addition, the module makes use of Accountscore services to enhance the dashboard.
Open banking can significantly enhance the current state of banking services and financial processes. However, since consumers lack faith in data security by banks for their personal and financial information, it has a long way to go in becoming the norm.
Digilytics’ RevEL is a potential solution as it extracts the information from bank statements and uses that data to help lenders in affordability analysis of the customer.
While you are here, other top articles you might be interested inDeliver First Time Right Applications with Documents & Online Data Role of AI in SME loan origination Verifying Information in Mortgage Lending Process: Supporting Documents vs. Data Sources? All about One-shot Learning AI technology and its impact on the UK Mortgage Market A change in technology is coming to the UK Mortgage 3C checks: The Digilytics Art of validating mortgage documents The Future of Computer Vision, Machine Learning and Artificial Intelligence in Mortgage Industry How industry 4.0 principles can work in the favor of mortgage origination? Top 5 Real Challenges in Building Predictive Models in Mortgages 5 Ways in which Mortgage Lenders can Leverage Digital Lending for Good