Why MLOps will boost the wider adoption of AI in financial services
By Srikrishna ‘Kris’ Sharma, Financial Services Industry Leader at Canonical
Machine learning (ML) and artificial intelligence (AI) are quietly transforming the way the finance sector works, spanning from identifying fraudulent activities to enhancing the overall customer experience. Current estimates indicate that around 32% of banks are already utilising AI technologies, and the global AI market in banking is projected to surge to $64.03 billion by 2030. Despite this potential, financial institutions face significant hurdles in realising the tangible advantages of implementing AI and ML on a large scale. These challenges encompass issues such as ensuring data quality, establishing model explainability, and addressing the critical concern of AI bias. This is precisely where Machine Learning Operations (MLOps) emerges as an indispensable solution.
MLOps help firms overcome some of the common hurdles faced when implementing AI, by providing a systematic approach to taking ML models to production, and maintaining and monitoring them. MLOps focuses on fostering collaboration among diverse teams involved in AI, ensuring seamless coordination between developers, data scientists, and IT operations teams. For banks seeking to explore AI, MLOps establishes a reliable framework that ensures improved code quality, swift and efficient patching, and streamlined release processes. It serves as the bedrock for establishing trustworthy and scalable AI in the financial services sector, playing a crucial role in facilitating the rapid adoption of AI technology.
The battle against fraud
Fraud poses a persistent and significant challenge for financial institutions, demanding early detection to mitigate losses and safeguard customers. In the United Kingdom, the impact of fraud has returned to pre-pandemic levels, with cases valued at £100k or higher surging to £1.12bn in 2022, reflecting a remarkable 151% increase from the previous year. To combat this rising threat, banks and financial institutions are increasingly leveraging AI-powered fraud detection models capable of analysing extensive datasets.
AI-driven fraud detection provides continuous monitoring of transactions and activities as they unfold in real-time. This instantaneous analysis enables rapid identification of potential fraud, empowering financial institutions to take prompt action to mitigate losses and safeguard customers. Beyond detecting ongoing fraudulent activities, AI models contribute to fraud prevention as well. By analysing historical fraud data and patterns, AI systems can identify potential vulnerabilities and offer proactive recommendations to strengthen security controls, minimise risks, and prevent fraud.
MLOps plays a pivotal role in facilitating the development and deployment of fraud detection models, encompassing various essential steps. With MLOps, data science teams can streamline and automate data collection and preparation processes, optimising efficiency. Moreover, it ensures the development of models in a consistent and reproducible manner, adhering to industry best practices for data analysis and feature selection. Once the model transitions into the production environment, MLOps guarantees scalable and dependable deployment, allowing teams to continuously monitor model performance, promptly identify any issues, and make the necessary changes to improve performance.
A personal touch
Many banks have made substantial investments in AI, but they often struggle to achieve a significant return on their investment. This can be attributed to several factors, including inconsistent customer data, limited knowledge sharing, and AI models with narrow scopes or limited replicability. To address this issue, banks must improve their ability to develop a comprehensive suite of machine learning (ML) models capable of driving personalised engagement at every customer touchpoint.
Currently, many ML models in banking are trained on isolated moments, focusing on short-term, product-driven objectives like increasing mortgage applications or account openings. However, to truly harness the power of AI for personalisation, banks need to shift their focus towards identifying the drivers of customer lifetime value and shaping customer interactions based on those insights.
MLOps can help ensure that the model is developed using best practices for data analysis, feature selection, and model training. In most cases, ML models and campaign-management systems often lack feedback loops to connect them, resulting in banks being unable to apply predictive insights from their ML models to inform campaign execution and decision-making. MLOps promotes collaboration between data scientists and various operations teams, helping to ensure that banks can confidently apply predictive insights to decision-making and create personalisation programmes.
Dealing with risk
Credit risk has always been a challenging area for banks, given the multiple factors that form an individual’s risk profile. Credit risk assessment involves analysing a borrower’s credit history, financial statements, and other relevant data to determine their ability to repay a loan. AI/ML are changing the way credit risk is assessed, with ML models getting increasingly accurate with each round of training.
However, ML models may still contain assumptions that can pose a significant challenge when analysing noisy historical financial data and may lead to poor model performance. There’s also a risk of overfitting the data, as ML models are more sensitive to outliers than traditional analytics. Feature engineering involves selecting and engineering the features used in the ML model. MLOps can help banks and financial institutions to help automate this process and ensure that the features are selected based on their relevance to the credit risk assessment task.
Despite their potential benefits, AI/ML models used in credit risk assessment face a critical challenge—bias. AI/ML models can inadvertently reflect biases and prejudices that exist in the data used to train them, leading to unfair or discriminatory outcomes. Addressing this issue is essential for maintaining fairness and ensuring ethical practices in lending. By leveraging MLOps, banks can detect and address biases in the data used to train the models thereby mitigating bias and promoting fairness in credit risk assessment models. MLOps enables thorough testing on diverse and representative datasets, ensuring that the models are not skewed towards any particular group or demographic. MLOps practices facilitate continuous monitoring and evaluation of the credit risk assessment models. This ongoing monitoring allows banks to identify and rectify any biases that may emerge over time, ensuring that the models remain fair and objective throughout their lifecycle.
Additionally, MLOps supports compliance efforts, enabling banks to adhere to regulatory guidelines and promote responsible lending practices.
Accelerating AI Adoption in Financial Services – The role of MLOps
MLOps empowers business leaders in the financial services industry to effectively harness the potential of AI, instilling confidence in stakeholders and regulatory bodies alike. By embracing MLOps practices, financial institutions can address common challenges associated with AI, such as transparency, reliability, fairness, and compliance. Through process automation and streamlining, MLOps significantly reduces the time and effort required for developing, deploying, and maintaining AI models, leading to enhanced cost-effectiveness and operational efficiency.
The advantages of AI adoption in the financial services sector are tangible and far-reaching, positively impacting areas such as fraud detection, credit risk assessment, and personalised customer experiences. With its transformative potential, AI holds the key to driving significant advancements in financial services, and MLOps emerges as a critical catalyst in accelerating the adoption and realisation of these benefits.
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