4 Benefits to Becoming Digital and Data-Driven in Financial Services

ANDREW DUNMORE | February 18, 2020

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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.

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BCU is a $2.3 billion full-service, not-for-profit, financial institution providing SEG and community banking to over 200,000 members in all 50 states and Puerto Rico. BCU is noted for setting new standards in bringing together technology and member service in the fast-changing world of financial services. As an organization, BCU is committed to improving members’ financial well-being through the brand promise We’ve Got Your Back.

OTHER ARTICLES

Coronavirus Hits Economy. How Fintech is Affected?

Article | March 7, 2020

From stock market swings and billions wiped off of the airlines, hotels, transportation revenues to drug, soap or iPhone replacement shortages – the coronavirus outbreak is taking its toll on many business aspects of life. Euler Hermes calls this a "quarantined trade". The company's analysts estimated that Covid-19 costs $320bn of trade losses every quarter. But what about fintech? It’s not immune to the virus either. As the side effects of Covid-19 will be unfolding in the weeks to come, we’ll see some fintech or finance companies taking hits. But other companies or solutions will be gaining traction.

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Is This A Fintech Bubble?

Article | February 24, 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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Ten steps to a maturity assessment-driven Data Strategy, for your Bank

Article | April 20, 2020

Here are ten steps to defining a data strategy based on a data capability maturity assessment, for a Financial Institution, Identify and Simplify Maturity models, and customize the yardstick as well as benchmarks based on local study and future organization strategy. Conduct workshop with leaders and grassroots to sensitize the assessment and questionnaires.

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Optimizing Risk Management With Machine Learning In Banking

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. 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

BCU

BCU is a $2.3 billion full-service, not-for-profit, financial institution providing SEG and community banking to over 200,000 members in all 50 states and Puerto Rico. BCU is noted for setting new standards in bringing together technology and member service in the fast-changing world of financial services. As an organization, BCU is committed to improving members’ financial well-being through the brand promise We’ve Got Your Back.

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