Published by Jessica Weisman-Pitts
Posted on March 28, 2024
Key Considerations for ML Ops in the Finance Sector with AWS Solutions
By Varun Narayan, Hegde Principal Engineer at Amazon
28 March 2024
The global machine learning as a service market size reached USD 22.86 billion in 2022 and is projected to reach an estimated USD 626.64 billion by 2032, growing at a double digit compound annual growth rate of about 39% from 2023 to 2032, according to Precedence Research. It is safe to assume that a notable portion of this market is the financial sector which is progressively embracing Machine Learning (ML) technologies, marking the dawn of a new data-driven era that enhances a wide range of functions from customer engagement, underwriting loans, trading securities, product customization and risk analysis strategies. However, the complexity of managing these advanced systems necessitates a robust approach to ML Operations (ML Ops). This discipline ensures the efficient deployment, monitoring for correctness, continuous improvement, and maintenance of ML models in production settings. For the finance industry, which is governed by strict regulatory requirements and demands utmost precision and reliability, ML Ops is especially critical. Here we delve into five key ML Ops considerations crucial for finance professionals to effectively manage and leverage ML technologies, with insights from leading financial institutions leveraging Amazon Web Services (AWS) products.
The financial industry is tightly regulated, with laws like the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) setting the tone globally. ML Ops strategies within the sector must give top priority to these regulations, ensuring models are transparent and where necessary, their decisions can be explained to both auditors and laypersons. Moreover, this adherence extends to ethical standards, requiring that ML models are developed and deployed in a manner that avoids biases, promotes fairness, and respects privacy. This approach not only complies with legal mandates but also builds trust with customers and stakeholders, cementing the role of ethical considerations in the financial sector’s ML Ops strategies. On the other hand ML models can also be leveraged to help monitor compliance to regulations through tools like anomaly detection in key metrics. In short any integration of ML in heavily regulated institutions must build such solutions keeping in mind, auditability, explainability and rule based compliance assurance as a fallback while keeping an eye toward future legal, regulatory and privacy challenges.
Quality data underpins effective ML models. In the finance domain, the precision, completeness, and integrity of data are directly tied to model accuracy. Establishing stringent data governance practices is imperative, covering everything from data collection and validation to cleansing and enhancement, ensuring datasets accurately represent current market conditions. Amazon SageMaker Studio, as utilized by Itaú Unibanco, significantly improved the speed, flexibility, and scalability of its ML infrastructure, transitioning from an on-premises setup to a more integrated development environment on AWS. This move not only reduced model development time from 6 months to 5 days but also enhanced data governance and model accuracy by providing a standardized, scalable infrastructure for ML model development.
[https://aws.amazon.com/solutions/case-studies/itau-ml-case-study] Effective data governance and integrity practices enable financial institutions to derive accurate insights, make informed decisions, and maintain a competitive edge in the fast-paced financial market. An important aspect of data governance is data access and security.
Most medium to large businesses in the financial sector process enormous volumes of data daily, necessitating that ML models not only remain accurate but also scalable. ML Ops must ensure both the infrastructure and the models are designed for scalability, possibly leveraging cloud computing and distributed processing to ensure both training on large datasets and inference at high transaction volumes. NatWest Group’s adoption of Amazon SageMaker, EMR, and S3 underscores the scalability and efficiency achievable with AWS services. By deploying nearly 100 machine learning models with plans for thousands more in the next two years, NatWest Group has been able to significantly enhance engagement with their 20 million customers while reducing costs, demonstrating the scalability and performance optimization capabilities of Cloud based ML solutions in the finance sector. The scalability and efficiency of ML models are crucial for handling the growing data and complexity of financial services, ensuring that institutions can quickly adapt to market changes and customer needs.
Given this sector’s attractiveness as a target for cybercriminals, ML Ops must prioritize security. This includes safeguarding sensitive information, ML models and training data details against breaches, reverse engineering of models over time, implementing advanced encryption, access controls, and conducting frequent security and compliance assessments.
Products like Amazon Macie represent a potent security solution that leverages machine learning to autonomously identify, categorize, and safeguard sensitive information held within AWS. By implementing suitable security measures, like encryption or access limitations, this service minimizes the likelihood of data breaches, thereby ensuring the protection of your information. Fraud detection is another popular application of ML in and beyond finance. It is imperative that such capabilities are developed with due consideration to customer trust, minimizing false positives and adherence to local laws. Mastercard’s use of AWS artificial intelligence (AI) and machine learning (ML) services exemplifies the power of AWS in enhancing fraud detection capabilities while adhering to regulatory standards. By leveraging AWS, Mastercard detects three times the amount of fraudulent transactions and has reduced false positives by tenfold, showcasing the effectiveness of ML and AI-based solutions in navigating the complexities of fraud prevention and preservation of customer trust. This proactive stance on security not only protects against financial loss but also reinforces customer confidence and trust in financial institutions’ ability to safeguard their assets.
Beyond the technical deployment of ML models, operationalization in finance means embedding these models into the fabric of existing business operations. It necessitates a collaborative effort among data scientists, ML engineers, IT, and business stakeholders to ensure smooth integration with business processes and systems. Itaú Unibanco’s migration to AWS and utilization of Amazon SageMaker Studio exemplifies such a successful operationalization of ML models in finance. By moving to AWS, Itaú not only accelerated its ML model deployment times but also improved staff productivity and cost efficiency, showcasing the benefits of Cloud-based ML services in the seamless integration and operationalization of ML technologies. Effective operationalization ensures that ML models deliver tangible benefits, enhancing decision-making processes, customer experiences, and overall business performance in the financial sector.
Conclusion
As ML becomes increasingly foundational in the financial industry, the role of ML Ops in managing these technologies grows in importance. By focusing on regulatory compliance, data integrity, model scalability, security, and seamless operational integration, and leveraging the right cloud services for ML, compute and storage, financial institutions can fully exploit the capabilities of ML. These considerations not only enhance the performance and reliability of ML systems but also prepare financial organizations to navigate the complexities of an increasingly digital and data-centric world.
About the Author:
Varun Narayan Hegde is a Principal Engineer at Amazon, where he leads groundbreaking projects in Amazon Retail’s Consumer Experience organization. With his expertise in machine learning and a USPTO-granted patent, he has been instrumental in developing an innovative offline model simulation and evaluation system. Varun’s leadership in the detailed design and execution has led to the successful simulation of over a million models since the invention. As a forward-thinking technologist, he continues to excel in his field, shaping a technology-driven future. Varun’s LinkedIn profile is https://www.linkedin.com/in/hegdevarun/
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