Machine Learning Operations (MLOps) in Banking | Deloitte US has been saved
With recent technological advancements, data has become an invaluable resource for organizations. Computational techniques like machine learning (ML) have enabled organizations to deliver better value to consumers at lower costs. Within banking, ML algorithms and analytics have improved end-user services, personalized customer experiences, prevented fraudulent claims, and boosted sales.
Firms use large volumes of historical data to train ML algorithms to predict outcomes or uncover patterns that generate crucial insights. For banks, managing the new data velocity and scaling of ML algorithms can become complex. “Stale” models, which are ML models trained on outdated data, can produce inaccurate predictions that can be harmful. Machine learning operations (MLOps) has emerged as a framework to address these complex challenges and enable ML at scale.
MLOps extends DevOps principles to machine learning operations to manage data and machine learning model updates due to rapidly evolving business processes. It is the set of practices, technologies, and skill sets that streamline the deployment, maintenance, monitoring, and training of models while also improving the collaboration between data scientists and operation engineers. There are four key tenets to deliver the foundational need for “continuous training and continuous delivery” for ML:
With 71 zettabytes of data generated in 2021 and dynamic world events changing consumer behaviors, data used to train models can rapidly become out of date. In a 2021 survey involving 750 business decision-makers, only 8% felt that their companies’ models were sufficiently sophisticated.2 This poses a serious challenge, and MLOps has become a necessity for banks as they serve consumer needs, aim to mitigate security and financial risks, and improve financial performance. MLOps helps banks in three facets:
Banks serve a wide range of consumers, ranging from individuals and small businesses to large corporations and governments. Machine learning has been instrumental in enabling banking institutions to process larger volumes of data faster than human rule-based systems to uncover subtle and hidden patterns in diverse data sets. For example, a global bank doubled its fraud detection capabilities by deploying ML models to identify anomalies from historical transactions.3 Also, a top European bank is using its rich datasets and cloud computing to build ML models that prevent fraud.4 In both examples, these ML models paired with human expertise have delivered better predictions to best serve their consumers.
With constantly changing consumer behaviors and needs, banks may find themselves at a disadvantage if their model outputs aren’t current. MLOps can help banks deploy better real-time models and improve their top-line results with key insights to cross-sell their products to the relevant consumers. With a rise in digital-only banks, MLOps can be a key enabler to help traditional banks stay relevant in retail banking by providing personalized services and experiences to their customers.
A major financial services institution in the United Kingdom leveraged MLOps to scale its data science abilities to build and launch ML services in a faster, scalable, and autonomous manner. Using Continuous Integration Continuous Delivery pipelines in the cloud, the financial institution was able to reduce the time from ideation to value realization by 60% and standardize the development and deployment of ML models across teams.5
In Europe, a collaboration of five Dutch banks used MLOps to standardize development and deployment of AI and ML to monitor payment transactions for signals that could indicate money laundering or the financing of terrorism. Being cloud-native, the banking consortium found it even easier to leverage ML, with collaboration and joint ownership of models across tech, business, and IT.
Today, traditional banks face an increasing threat by modern digital-only banks that operate with significantly lower capital expenditures, more personalized services, and faster product delivery. For traditional banks to stay competitive and increase their value by leveraging customer insights, MLOps is a critical requirement. However, MLOps adoption across the banking industry is low and is often overlooked until scale challenges emerge. The low adoption rate provides first movers an opportunity to occupy a strategic advantage, though. By adopting MLOps, these companies enable their data scientists to rapidly deploy models and better meet the dynamic needs and regulations within the global marketplace. A strong MLOps competency will future-proof a bank’s business and distinguish it as a world-class institution delivering top-tier experiences for its consumers.
1. Akinwande Komolafe, “Retraining model during deployment: Continuous training and continuous testing,” neptune.ai, February 21, 2023.
2. Petroc Taylor, “Total data volume worldwide 2010–2025,” Statista, September 8, 2022.
3. Wall Street Journal Custom Content, “At Capital One, enhancing fraud protection with machine learning,” March 1, 2023.
4. Barclays, “Machines can’t do it by themselves,” January 16, 2020.
5. Maira Ladeira Tanke et al., “How NatWest Group built a scalable, secure, and sustainable MLOps platform,” AWS, April 26, 2022.
As the chief cloud strategy officer for Deloitte Consulting LLP, David is responsible for building innovative technologies that help clients operate more efficiently while delivering strategies that enable them to disrupt their markets. David is widely respected as a visionary in cloud computing—he was recently named the number one cloud influencer in a report by Apollo Research. For more than 20 years, he has inspired corporations and start-ups to innovate and use resources more productively. As the author of more than 13 books and 5,000 articles, David’s thought leadership has appeared in InfoWorld, Wall Street Journal, Forbes, NPR, Gigaom, and Lynda.com. Prior to joining Deloitte, David served as senior vice president at Cloud Technology Partners, where he grew the practice into a major force in the cloud computing market. Previously, he led Blue Mountain Labs, helping organizations find value in cloud and other emerging technologies. He is a graduate of George Mason University.
Chris is a principal in Deloitte Consulting LLP’s global technology practice and is the US banking sector leader for Cloud. He brings 20 years of strategy consulting and hands-on transformation experience in the cloud and core technology infrastructure domain, specializing in financial services. Chris has extensive experience partnering with senior executives to develop and implement large scale technology transformations, cloud-centric operating models, strategic cost optimizations, global outsourcing programs and workforce of the future initiatives. He brings a perspective that humans and machines must develop a symbiotic relationship, each with specialized skills and abilities, in a unified workforce that delivers multifaceted benefits to the business. Chris writes frequently about emerging technologies and banking and his observations appear in Wall Street Journal’s CIO Journal, Deloitte Tech Trends, as well as financial services industry forums and technology journals. He holds a B.S. from Miami University (Ohio) and a M.S. from Northwestern University.