Article | April 27, 2020
Everyone wants to build a creamier, faster, and more efficient financial services journey — which in 2020 is not a point of controversy or friction.
Today, customer demand is touching peaks. It is customer demand that forces businesses come out of their silos and collaborate with others to create products and services that are open-source, non-proprietary, and do not lock down users into an ecosystem. The launch of Open Banking is initiated to fundamentally change the way consumers, businesses, and banks pay and get paid, and how they maintain their data. The foundation of a unified Application Programming Interface, or API, across financial institutions, constitutes a foundation in which data can be seamlessly and securely shared right away.
While open banking is in the initial stages of its evolution, many assume this trend to expedite and reshape the banking industry in significant ways. Thanks to open banking developments around the world, customers are becoming more informed of the essential value of their information and are increasingly seeking more command over their financial data.
Table of Contents
•Why is Open Banking Important?
•How Does Open Banking Work?
•Open banking in United States
•The Wave of Change in Payment Arena
•Cloud-based Processing Services
Why is Open Banking Important?
The most valuable asset in the 2020 world is data, and banking data is the finest of the crop, as it facilitates insight into how consumers and businesses are employing money, saving, and acquiring debt.
The data has got value and the data that the bank holds and the customers, belongs to the customer and not to the bank, that’s a fundamental realism or premise that the government has is writ large in European legislation. You will be pestered by its called GDPR but fundamentally enshrines the fact that the data belongs to you, the consumer or to the SME, not to the financial institution. And if you as a consumer want to use that information to get access to better products and better services, it’s entirely your right to do so. That’s what open banking is trying to deliver. It holds the promise of making finance more convenient, better tailored and fundamentally smarter.
From industry point of view, open banking promises to lower the barriers to entry to financial services and lower the barriers to innovation in financial services. That’s why it is so exciting for many of the fintechs.
Open Banking delivers enormous opportunities in 2020, for the fintech ecosystem that goes beyond necessary to invigorate customer relationships and transform businesses. Through ecosystem partners, banks can enter customer journeys earlier than before and create added value to expertly serve enduring customers as well as attract new ones. Customers foresee seamless digital experiences, and platform-based business models, that are a quintessential element of the digital economy. When embracing the opportunities Open Banking brings, banks can leverage the ability, speed, effectiveness, and innovativeness of startups to enhance their product and service offerings. Banks also have access to other banks’ data. By genuinely performing multi-banking services, they can drag competitors’ customers and spread awareness of their brand.
How Does Open Banking Work?
Let’s put this into three:
• What the banks do
• How you get registered
• What the customer sees
The banks have put into places API’s, this means they have made huge technology decisions to expose customer data and access the data from other third parties. For open banking to work, you have to be governed by the OBIE rules. The OBIE is open banking implementation entity and you can either be an AISP or a PISP that sits under the OBIE. The AISP essentially means you are an account information services provider and PISP means you are payment initiation services provider. One means you can aggregate transaction data and customer data, the other means the payments that you can initiate from your third party, from your bank. The third element to this is TSP, a technology service provider. And they basically provide all the rails between the banks and between third parties to make sure that this whole system runs right. From the consumer perspective, at the end, it gives them the ability to share their data with third parties but crucially have the permissioning power to be able to do that.
An AISP can condense reams of bank account statement data and pass it to the customer in a single interface, making it ideal for treasurers of multi-banked organizations. Payment service users – whether they are individuals or businesses, can guide their banks or payment service providers to share their bank balance and transaction data with regulated AISPs. To display this information on a user-friendly dashboard, the AISP can convert all this transaction data into the expected format and send it to the customer’s ERP or Treasury Management System.
Before the initiation of Open Banking, businesses and consumers were logging into each bank individually to initiate payments, using various workflows and security etiquettes. With the arrival of Open Banking, individuals or businesses are now equipped to mandate their multiple banks or payment service providers to receive payment instructions via their PISP’s app.
Learn more: Open banking in the same language
Open banking in United States
According to Deloitte Insights, The open data revolution is most obvious in Australia, the United Kingdom, and other countries in the European Union. Each has distinct regulations that require banks to share customer data with third-party providers as per customers’ instructions. Other countries, such as Canada, Japan, and Singapore, are also considering similar regulations. Australia, however, has taken it a step further: It has gone beyond the financial services sector, applying an expansive set of rules on consumer data rights and data-sharing to other industries as well. We do not know yet whether this will be a model for other countries, although, in the United Kingdom, similar efforts are underway.
While the open banking model in the United States may take a different path, US banks can learn valuable lessons by looking at how it has been implemented in more regulatory-driven environments. Bank leaders may find it particularly helpful to review how different regions set technical and customer experience standards for data-sharing.
To date, there are no signs that new open banking regulations are being developed in the United States.
Learn more: Open banking model strategy
The Wave of Change in Payment Arena
One interesting example of the innovation encouraged by Open Banking is HSBC’s Connect Money application. This application enables customers to view all their accounts within single application-even if those accounts are scattered across different banks.
According to an article by Accenture "How Open Banking is Catalyzing Payments Change" Connect Money demonstrates one of the most fascinating features of Open Banking. Many Open Banking products and services are subject to “network effects”—they become more valuable as more banks participate. If Connect Money allowed customers to track only HSBC accounts, it might have been somewhat useful. The fact that the app connects across many banks is what makes it powerful.
This aspect of Open Banking can also make it easier for new entrants to grow and gain purchase in the market since more access to data means more opportunities to create value for customers.
In the payments platform, Open Banking is advantageous to small and medium-sized businesses (SMEs). This is because it facilitates account aggregation, better financial management, easier credit checking of customers, and the unification of lending and accounting applications. With Open Banking, SMEs can receive and make payments using different platforms with better clarity and best momentum.
Open Banking payments are validated instantly between consumers and their banks. This means the chargebacks that merchants must pay because of fraud or rejected payments becomes zero. This offers plentiful savings for all merchants. Payments powered by Open Banking also give real-time credit transfers, confirming the payment and empowering merchants to ship the product immediately.
Cloud-based Processing Services
Open Banking also maintains cloud-based processing services- a compelling alternative for decentralizing processing and encouraging payments innovation. The benefit includes:
• More economical costs
• More regular compliance maintenance
• Advanced enterprise agility
• The capacity to flex volumes quickly
The new payment option, called IATA Pay, provides customers more extensive selection of payment methods when buying airline tickets. The most popular services that are being worked in 2020 covers Request to Pay and P2P payments services. We can anticipate seeing many more in the following years.
Open Banking scales to opportunities preferably, then threats. Done perfectly, banks can flourish, encouraging their customer franchises and brand, securing a defined culture, and fostering business through open collaboration with the world beyond financial services.
We are witnessing the initial stages of a seismic industry migration that will come into full power over the next five years. The evolution of innovations with the potential to force simplicity and enhance flexibility is turning a once complicated web of financial institutions into centralized tools to maximize value creation. Open Banking scales to opportunities preferably, then threats. Done perfectly, banks can flourish, encouraging their customer franchises and brand, securing a defined culture, and fostering business through open collaboration with the world beyond financial services.
Consequently, any bank that needs to stay consistent in 2030 must begin to design their Open Banking strategy immediately.
Article | March 4, 2020
The short answer is yes…. sometimes to both. However, there is a huge change underway and enough evidence to suggest that customer and competitive pressure alone will drive more change, even without further regulatory updates. Globally, and in the UK, margins in the lending market are feeling the pressure. Slow credit growth (currently at 2.5% in the UK), and low interest rates are the primary drivers. This has fueled competition, especially for lending products with higher margins, such as mortgages.
Article | May 26, 2021
The enormous amounts of data accessible to banks and their high demand for forecasting make the financial industry a perfect area for machine learning (ML) to shine. In this article, we explore the current applications of machine learning in banking when it comes to risk management, define its challenges and provide a future outlook.
Credit Risk Management
For the past few decades, banks have mostly used logistic and probit regression models for credit risk assessments and internal risk management. However, all conventional models inherit the same flaw — they predict outputs based only on linear relationships between input variables.
This limitation was exposed in the catastrophic 2008 housing market crash. Although the crisis’s negative consequences have been multiplied by uncontrolled sales of credit default swaps and other complex financial instruments, the fundamental reason for failure was in the inaccurate credit risk model.
In the aftermath, with the intent to force financial institutions to provide more detailed reports, The Federal Reserve’s CCAR now requires banks to account for more than 2,000 economic attributes. Consequentially, this also led to other regulating authorities introducing new standards that improve supervisory data quality and reporting.
At the same time, with the proliferation of banking apps, social media, and digital communication overall, financial institutions now collect lavish amounts of unstructured data. If gathered and processed correctly, these new datasets can help gauge critical insights for a wide range of banking operations.
This is where machine learning comes into play. More advanced non-linear approaches to credit risk modeling including neural networks enable banks to make predictions with a previously unseen level of accuracy and granularity.
The utter superiority of machine learning over traditional credit risk modeling approaches comes at the cost of the prevailing ‘black box’ problem. While we can decide to trust ML algorithms based on statistical evidence of their feasibility, current regulatory constraints won’t allow it to happen.
However, machine learning can still be used to a great extent while being regulation-compliant. Even simple linear machine learning approaches still yield more accurate results than conventional ones. Many banks also use unsupervised machine learning methods to explore data, while using traditional classification and regression models to make predictions.
Fraud Management and Surveillance
Nowadays, the majority of banks’ fraud detection systems use rule-based approaches. This causes banks to deal with a significant number of false positives, forcing them to spend inordinate amounts of resources to distinguish meaningless behavioral deviations from real threats.
The ability of machine learning to capture subtle trends and uncover non-linear relationships allows banks to get a complete picture of a client’s activity and significantly lower the probability of false positives. For example, by integrating ML into its fraud detection model, Danske Banks managed to reduce false positives by 60%.
Similar to many other AI-based solutions in the financial space, the biggest adoption hurdles concern regulations and the unexplainability of AI systems. For example, depending on the jurisdiction, banks are often unable to provide developers with sensitive information related to past breaches. Next, the outputs of unsupervised monitoring systems sometimes can’t be explained, which makes them non-compliant.
However, financial institutions have found a way to at least partly leverage the power of ML for fraud management. A fraud prevention system’s alerts will still be triggered by rule-based models, but the integration of an ML algorithm on top of them can allow adjusting surveillance methods to a person’s behavior fluctuations. Such ML models are typically less complex and explainable, which makes them applicable in a regulatory context.
Article | March 30, 2020
As the pandemic known as Covid-19 is changing the way we live our lives, there are a couple of innovative technologies that have made this transition period much easier. We don’t know much about this virus but so far it has completely turned the world upside down. Not only are we running a risk of overwhelming our healthcare systems and possibly losing thousands of lives to this virus, the effect that COVID-19 has had on the economy could easily be compared to the 2008 crisis.