Fit for Digital: Banking in the Age of Disruption

| August 3, 2017

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How can banks thrive in the face of unprecedented change and unlock the benefits of digital?  Learn more with Fujitsu's Anthony Duffy.

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OnDeck

At OnDeck, we make small business a big deal. Our passion for Main Street and the cutting-edge technology we use to evaluate businesses based on their actual performance, not solely business owners’ personal credit scores, make it possible for us to responsibly expand access to credit. This allows businesses to spend their time where it provides the most benefit on their customers, not looking for a small business loan. Since 2007, we’ve issued over $8 billion in capital. As the largest online small business lender in the U.S. serving more than 700 different industries, we have been trusted by over 70,000 small businesses by providing them with a term loan or line of credit to help them build a growing and thriving enterprise.

OTHER ARTICLES

6 Digital Banking Best Practices During the COVID-19 Outbreak

Article | April 7, 2020

As the financial industry navigates the uncertainty of the COVID-19 pandemic, one thing is clear: digital banking has never been more important to financial institutions and their customers. While digital channels like mobile banking apps have always offered convenience, they now offer physical safety as well. With a digital approach to these extraordinary circumstances, banks and their customers can rest assured that social distancing does not mean financial isolation. Here are six best practices your institution can use to encourage and enhance digital banking channels given the unprecedented nature of COVID-19.

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Is This A Fintech Bubble?

Article | April 7, 2020

Before I begin…good news for people nervous about the stock market panic today…. CNBC is airing a ‘Markets in Turmoil’ tonight. It never has failed at producing an investable bottom. If you are confused or angry…you might be too heavily weighted in stocks. The warning signs have been epic. Read Ben Carlson’s excellent piece titled ‘markets have always been rigged, broken and manipulated ‘.

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Are challenger banks transforming the lending market or just providing digital makeup?

Article | April 7, 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.

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Optimizing Risk Management With Machine Learning In Banking

Article | April 7, 2020

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. Challenges 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%. Challenges 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.

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Spotlight

OnDeck

At OnDeck, we make small business a big deal. Our passion for Main Street and the cutting-edge technology we use to evaluate businesses based on their actual performance, not solely business owners’ personal credit scores, make it possible for us to responsibly expand access to credit. This allows businesses to spend their time where it provides the most benefit on their customers, not looking for a small business loan. Since 2007, we’ve issued over $8 billion in capital. As the largest online small business lender in the U.S. serving more than 700 different industries, we have been trusted by over 70,000 small businesses by providing them with a term loan or line of credit to help them build a growing and thriving enterprise.

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