Article | April 15, 2021
Open Banking is all about the customer being in control of their data and funds. It gives them the freedom and flexibility to decide when and with whom to share their valuable information. However, as with all vibrant and progressive ecosystems, speed, security, and ease of use will determine open banking’s future success along with the key issue of trust. Will the end user trust people to share data with them and trust their banks to still protect their data?
PSD2 Open Banking gives Payment Service Users (PSUs) the legal right to share their transactional account data with regulated third party providers (TPPs). For this to be possible, the 6,000+ Financial Institutions providing transactional payment accounts that can be accessed online have to put in place open banking APIs. These APIs give TPPs the access required to either make payments on an account holder’s behalf or view account data and funds, both of which require the account holder’s prior explicit consent. Access can only be denied if a TPP is believed to be unauthorised or fraudulent.
Open banking regulation has given rise to a new group of FinTechs who are seizing the opportunity to create innovative apps and products with the customer at the core of the offering. At the end of 2019, 240 TPPs from across the EEA and UK were regulated to provide open banking services. A year later, this figure had increased to 450 (excluding the thousands of credit institutions that are also able to act in the capacity of TPPs). The near doubling of newly regulated entities demonstrates user demand for the innovative products and services that these organisations are offering – it is now down to trust and security in the ecosystem, along with ease of use, to drive volumes.
The ability for TPPs, many of whom may be unknown to these Financial Institutions, to request immediate access to valuable data and funds presents many challenges and risks – all of which must be addressed without introducing potential friction in the customer journey. The main challenges are knowing if a TPP is who it claims to be and whether it is regulated to provide the services being requested at the time of the transaction request. After all, these are the key factors enabling the bank to trust the TPP and feel confident the end user can trust them. The added difficulty of knowing which markets within the EEA a TPP is authorised to operate in is an additional challenge.
Financial Institutions have long been the trusted guardians of their customers’ data and funds. Although the open banking model means the customer now has ultimate control of their data, it is still primarily the Financial Institution’s responsibility to ensure nothing goes wrong and they are likely to be held liable in any disputes that arise. There is also the very real reputational risk to Financial Institution if something does go wrong.
Checking a TPP’s identity, its current regulated status, and the services it is requesting to perform are essential but not easy tasks to complete in that, firstly, a Financial Institution needs to determine whether a TPP is who it claims to be. This is done by having real-time access to the 70+ Qualified Trust Service Providers (QTSPs) who can issue PSD2 eIDAS certificates. These eIDAS certificates contain the requisite information on a TPP’s identity and are used to secure communications between Financial Institutions and TPPs. They also digitally seal messages, ensuring the integrity of the concept and proof of origin.
However, an eIDAS certificate can have up to a two-year validity period. During this time, changes may have been made to a TPP’s regulatory authorisation status by its Home National Competent Authority (NCA). This introduces significant risk to the Financial Institution’s decision process.
eIDAS certificates also do not contain information on the countries a TPP is authorised to provide their products and services into under passporting rules. This information is held on the TPP’s Home NCA Credit Institution and Payment Service Provider (PSP) registers. Between them, the 31 NCAs maintain over 115 databases and registers. Checking them at the time of a transaction request is paramount to prevent fraudulent TPPs from slipping through the net.
According to the Konsentus Q4 2020 TPP tracker, every country in the EEA had at least 75 TPPs who could provide open banking services. These may not all be Home regulated TPPs. Take, for instance, Germany, who had 35 Home Regulated TPPs in December 2020 but an additional 112 TPPs who could passport in their services. To do the requisite due diligence on all these TPPs would require having online access to all the databases and registers hosted by the NCAs regulating these TPPs. This means connecting to the 31 NCAs and interrogating over 115 separate registers in real-time, in addition to connecting with all the QTSPs who issue PSD2 eIDAS certificates.
When a Financial Institution is presented with an eIDAS certificate by a TPP, if a real-time online connection can be made to all the legal sources of record, the Financial Institution can make an instant informed risk management decision on whether, or not, to give the TPP access. All this can be done behind the scenes without the end user even being aware of what is happening.
As volumes look to dramatically increase over the next few years fraudulent and other sorts of attacks are bound to increase. Financial institutions are going to face increasing challenges around protecting end users’ data, ensuring access is only given to those with the appropriate authorisations and permissions. A very real risk for them is the reputational one; after all, end users may not be that good at separating a reputational issue around open banking from broader issues around their banking relationship.
For Financial Institutions, maintaining trust in their brands is going to be crucial going forward, but the risks are going to increase if they have not locked down who can access end user account data and funds.
Article | April 15, 2021
Digital transformation can mean different things to each financial institution (FI). For some, it’s a push to modernise legacy systems and acquire fresh talent. For others, a journey to adopt an organisational strategy that unites departments and teams. No matter the motive, nearly all FIs want the same result—to drive efficiency, revenue and cost savings. For many forward-thinking FIs, artificial intelligence (AI) is a key part of this process.
At first, implementing AI can feel like an arbitrary effort that requires too many stakeholders, too much technology, and too big a transformation. Yet, as AI in banking matures, it brings the potential for higher-complexity solutions that generate positive ROI across business segments. A recent financial services study showed that 85 percent of respondents had successfully implemented AI within their organisation. [i]An additional 64 percent plan to use AI across a wide variety of use cases including process automation, risk management, and new revenue generation.
These studies prove that AI is not only becoming more mainstream, but is necessary to help FIs achieve their business goals, strengthen customer relationships, and remain competitive. To demystifying AI and reap its benefits, FIs must embrace a multi-functional strategy that sparks innovation and encourages collaboration. The three use cases below show how AI can be implemented to most immediately impact a financial services organisation.
Elevating employees, not replacing them
AI has historically created fears of job loss and obsolescence. However, within financial services, AI is well equipped to automate manual and repetitive tasks - such as rekeying data - rather than autonomously make critical financial decisions on behalf the organisation. Due to the complexity of the decisions, degree of regulation, and importance of qualitative factors these tasks are – and for the foreseeable future will be – better managed by employees.
A key use case of AI for automation is the utilisation of optical character recognition to streamline the process of spreading financials when underwriting commercial loans. Previously, credit analysts would have to invest hours painstakingly transferring borrower financial data into various systems, reducing time for holistic credit analysis and increasing loan underwriting times. However, by employing AI-driven solutions in combination with powerful workflow automation, banks have been able to significantly increase efficiency in lending processes, reducing processing and cycle times by more than 50 percent and seeing a 10 percent increase in front-office capacity to focus on true value-add analysis and customer relationships. [ii]
Additionally, AI has the ability to empower bankers, not only by eliminating manual tasks, but also by being able to equip them with powerful insights around relationship profitability and credit risk. By refining sophisticated, machine-learning based models, banks can more accurately predict and leverage metrics such as probability of default and loss given default within risk-based pricing models to provide competitive lending rates to borrowers, while still maintaining healthy profitability at the relationship and portfolio level.
Reshaping customer engagement
The acceleration of digital banking due to the COVID-19 pandemic and the rise of customer-centric titans have put significant pressure on financial institutions to modernise and reshape their approach to digital banking to appease rising customer expectations.
Customers not only expect a frictionless and seamless onboarding process, but for their bank to act as an ever-present financial advisor, offering personalised insights on spending habits, money management and financial decisions. AI-powered virtual assistants and chatbots offer new levels of accessibility to common questions by utilising natural language processing to find past transactions, access credit scores, and view balances. However, institutions can take a further step of both anticipating customer needs and offering targeted product suggestions based on propensity scoring models. Proactively offering recommendations can be helpful to customers due to the complexity of different financial products and enables banks to simultaneously satisfy customers while unlocking new revenue opportunities.
FIs can leverage AI to operate as a dedicated advisor, offer a differentiated customer experience, and reduce customer churn. However, equally important to the underlying predictive models is having a single, end-to-end platform to drive direct actionability by delivering insights to the right banker at the right time.
Boosting back-end efficiency
In addition to empowering employees to focus on true value-added activities, AI offers enhanced methods to improve operational efficiency and risk management. Analysis of IDC data shows that AI technologies can improve the cost efficiency of financial institutions by over 25 percent across IT operations[iii].
As a part of fraud detection, institutions can leverage AI to oversee thousands of transactions and efficiently flag anomalies that are indicative of fraud. Historically, transaction monitoring has struggled with false positives, through which genuine transactions are incorrectly flagged. However, through machine learning, actual, fraudulent transactions can be compared to false positives, which can then be fed into the model, improving accuracy over time as the system incorporates learned, differentiating factors.
Finally, the COVID-19 pandemic has caused institutions to revaluate how they assess credit risk and problem loan management. Lenders have had to segment their portfolio by geography and industry to differentiate sectors which were more severely affected by the pandemic versus those that were less challenged. Additionally, there is now a greater emphasis on utilising real-time and transactional data in addition to other data sources to truly understand business performance and borrower resilience. As the uncertain pandemic recovery continues, leveraging AI-powered predictive models in combination with delinquency tracking, credit migration modelling, and other tools will continue to be critical to align actual portfolio risk with the risk appetite of the institution.
As AI adoption continues to mature, FIs should avoid sporadically focusing on isolated use cases. Instead, organisations should strive to align strategy, organisational culture, and digital infrastructure under a united AI strategy. This will help enable them to capitalise on revenue growth, operating efficiency and cost savings, from the front to the back office, and across all lines of business.
Article | April 15, 2021
In the following analysis, we take a look at why Tesla (NASDAQ:TSLA) could be an attractive acquisition target for Google (NASDAQ:GOOG). We break down our analysis into three parts: what Tesla would stand to gain, what Google would stand to gain, and a scenario where Tesla’s value could rise to $1.5 trillion aided by a deal with Google.
Article | April 15, 2021
With plenty of post-recession anti-banking sentiment still lingering, it’s common to see fintech and traditional banks framed in oppositional terms. There’s some truth to that, especially with disruption-minded digital-only banks, but technological innovations have transformed banking of all stripes — and nowhere is that clearer than with artificial intelligence.