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 | May 26, 2021
Technological innovation over the recent decades has attracted increased interest of researchers and industry experts thereby becoming an integral part of everyday lifestyle. The challenge lies in resolving the dispute between a forward-looking innovative structure promoting innovation, and a proportionately meticulous schema that is capable of winning faith of consumer.
Information technology is fast turning to be a key instrument in the lives of consumer across generations. In current global economy, customers across industries have been pampered and the credit goes to “Bigtechs ” like Ali Baba, Apple, Amazon, E-bay, the list being exhaustive along with “Fintech ” and “Incumbent banks ” as a result, consumers expect swift product delivery with flawless service.
A review of available literature is suggestive that further expositions tend to focus on fintech and its integration in banking arena investigating the factors that underpin the choice of external partners to collaborate, design, develop and implement fintech capability while addressing the gap between research and industry evolution.
Recent developments in technology have refurbished global economies at an immensely fast pace, making the business environment extremely challenging with continued margin pressure. Digital technologies are intrusive to not only the competition but also to the role of payments in businesses impacting the ultimate consumers.
Investigating digital transformation has been of continuing interest across industries. Digitization might abolish some vital job roles, threatening the human workforce reluctant to digital changes. However, observations are indicative of focus towards higher-value tasks and creating unprecedented opportunities. For instance, adoption of digitization in financial industry, provides considerable opportunity to relationship managers to spend minimal time in operational activities and maximum towards advising customers.
Amongst many ideas laying the foundation of fintech adoption, a growing body of literature recognizes two vital causes for the evolution of fintech companies that can be routed back to a decade. Firstly, the global economic financial crisis also called economic recession that has distinctly exhibited to consumers the flaws of the traditional system. Second, the evolution of new technologies that boosted mobility, easy flow of information, speeding up the service delivery and lowering the costs.
The way banks engage the customer today has gained fresh prominence, with a movement from branch banking to digital systems, benefitting from customer insights. Aiming to enhance consumer engagement and gain competitive advantage, debates continue about the best strategies banks adopt to engage with customers that has resulted in adding capabilities and complex technology on top of systems and processes to meet dynamic customers’ expectations to gain real time personalization.
Much debated question is whether organizations with traditional framework are able to come up to such expectations becoming capable of disrupting industry by prompt digital delivery using advanced algorithms and digital platforms to successfully provide unrestricted access to information bits. Promising superior experience to users, however, if industry experts hit the bulls-eye and tend to offer more competitive prices, enhanced operational controls may entitle lesser risk and probability of higher revenues.
Such developments bring along advantages and disadvantages at the same time. Whereas advantage lies in reduced transaction processing times, service excellence and global integration the disadvantages lie in the fact that not many users are keen to shift to the fintech modes as far as their financial transactions are concerned since they are apprehensive of the risks associated with such adoption, witnessing this paradigm shift in the pace at which industry is developing focussing on much saturated red ocean of retail banking and gradually making a shift towards payment systems, which seems to be an untapped blue ocean of opportunities.
The underpinning factors that govern the financial industry are KYC / AML and CFT together forming the basis for regulatory controls. Ethical transparent business knit together with service excellence, minimal risk, a strong regulatory framework in the competitive industry has given the incumbent banks an opportunity to partner, collaborate and codevelop with technology and consulting firms for collaborative innovation.
Technologies like artificial intelligence, blockchain are capable of providing effective product suite to clients resulting in scalability of business.. On the other hand, robots replacing front end customer interface causes threat of redundancy to human capital and increase the training costs. As a result, many times an informal approach to collaboration tends to delay the outcome since the senior management looks at digitization in transaction banking as a profitable step and the middle management is hesitant of human redundancy, training etc which might cause delay.
Taken together, a probable explanation advocates that common goals of digitization in financial institutions are regulatory control, risk mitigation, increase in revenue and to meet dynamic customer expectations by co-developing with external partners to gain competitive advantage. This adoption may further lead to higher cohesiveness in departments, improved value chain and reduced turnaround time with higher resolution quality.
The digital transformation is capable of reducing the operational costs and overheads leading to increased profits, improved efficiency, better regulatory controls with less risks and collaborative opportunities for partners taking benefit of its tech talent to reach desired results.
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Article | May 26, 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 | May 26, 2021
Make no mistake: the current explosion of volume in data coupled with the emergence of new tools and processes on the public internet has created a near-perfect storm when it comes to generating opportunities for the enterprise. In addition to the creation of all new forms of business – think of Netflix and Uber – the abundance of expansive data that can be utilized in cost-efficient applications can also drive insight and innovation in previously unimaginable ways.