The financial industry is undergoing a significant transformation through the adoption of advanced technologies like Azure Databricks, AI, and machine learning. Organizations are turning to these tools to stay competitive and meet rising customer expectations. These technologies enable enhanced data processing, real-time analytics, and predictive modeling, which help financial institutions improve security, streamline operations, and deliver personalized services.
Companies are using these tools to detect and prevent fraud, gain deeper insights into customer behavior, and make more informed investment decisions. The following case studies of SWIFT, Icatu Seguros, and Voya Financial showcase the transformative impact of these solutions, setting new standards in the financial sector.
Artificial intelligence (AI) and machine learning (ML) are revolutionizing the finance industry, driving significant advancements in risk management, customer experience, and operational efficiency. At their core, AI refers to the development of computer systems capable of performing tasks that typically require human intelligence, such as learning, problem-solving, and decision-making. ML, a subset of AI, involves the use of algorithms and statistical models to enable machines to learn from data and make predictions or decisions.
The finance industry has been one of the earliest adopters of AI/ML technologies, leveraging these tools to stay competitive and meet their customer expectations. Financial institutions are utilizing AI/ML to enhance risk management, detect and prevent fraud, and deliver personalized services. By utilizing AI/ML, financial companies can process vast amounts of data in real-time, gain deeper insights into customer behavior, and make more informed investment decisions.
AI/ML technologies offer a wide range of applications in financial institutions, transforming various aspects of their operations:
The integration of AI/ML technologies in the financial services industry brings numerous benefits:
Below we have three examples of real-world use cases of artificial intelligence and machine learning used by large financial services companies.
SWIFT utilizes Azure Machine Learning to bolster its fraud detection capabilities, providing a robust security framework for over 11,500 financial institutions worldwide. This case highlights the essential role of real-time analytics in maintaining the integrity of global financial transactions.
Various machine learning techniques, such as supervised learning, unsupervised learning, and reinforcement learning, are employed to address different challenges in the financial sector.
SWIFT, the Society for Worldwide Interbank Financial Telecommunication, is a global leader in secure financial messaging services. This member-owned cooperative provides a secure infrastructure for money and security transfers, serving over 11,500 financial institutions worldwide. SWIFT's network and platform are trusted to deliver seamless automated transmission, receipt, and processing of more than nine billion financial messages annually, playing a crucial role in the global financial system.
SWIFT faced the challenge of detecting and preventing fraud in real time while managing the high volume of transactions processed daily. As the industry provides faster payment solutions between individuals and organizations across borders, the risk footprint increases, as do costs. This is especially true when accounting for secondary impacts like fraud remediation and fund recovery, making fraud a massive issue for all financial institutions and their customers.
“Fraud in payments is a major concern for our clients—in the order of magnitude of billions of dollars every year. Preventing financial crime is a problem that we cannot solve individually as a bank, we need to solve it collectively.” - Isabel Schmidt, Co-Head of Global Payments Products at BNY Mellon, a member of the SWIFT network.
“We connect financial institutions around the globe to our network and deliver products and services in over 200 countries. Our standards are used for financial institutions and banks to communicate with each other, which puts us in a unique position to bring the industry together to try to solve some of the most complex problems,” - Tom Zschach, Chief Innovation Officer at Swift.
To address these challenges, SWIFT integrated Azure Machine Learning into its operations, utilizing advanced machine learning algorithms to develop sophisticated fraud detection models. This advanced technology allowed SWIFT to develop sophisticated fraud detection models capable of analyzing transaction patterns and identifying anomalies in real-time. Azure Machine Learning provided the tools to build, train, and deploy these models efficiently, ensuring they could adapt to new threats quickly.
Key components of the solution included:
In collaboration with Microsoft, SWIFT utilized federated learning techniques in Azure Machine Learning combined with Azure confidential computing, Microsoft Purview, and a Zero Trust-based policy framework. This approach allowed SWIFT to build a highly accurate anomaly detection model for financial transactional data without copying or moving data from SWIFT members’ secure locations. Participants’ data remained confidential while the new model detected anomalies and gained new insights to help predict and prevent financial crime.
“Our first ambition at SWIFT is to build a foundation model for anomaly detection that underpins the detection and prevention of fraud. Our ultimate goal is collaborating with Microsoft and our community to start thinking about how we can stop fraud occurring in payments. We are exploring the federated learning aspects of Azure Machine Learning where we take a model developed by SWIFT and further train and enrich it with additional customers’ data through Azure confidential computing.”
“Using Azure Machine Learning, we can train a model on multiple distributed datasets. Rather than bringing the data to a central point, we do the opposite. We send the model for training to the participants’ local compute and datasets at the edge and fuse the results in a foundation model,” - Johan Bryssinck, AI/ML Product and Program Management Lead at Swift
The integration of Azure Machine Learning had a profound impact on SWIFT’s operations:
The SWIFT case study is available on the Microsoft website, and you can access it by clicking this link:
Icatu Seguros, one of the leading financial services companies in Brazil, employs Azure Databricks to modernize its data analytics and processing capabilities, enabling the creation of personalized insurance products and improving operational efficiency. This case study illustrates the power of scalable data solutions in transforming the insurance industry.
Icatu Seguros, a 30-year-old, 100% Brazilian capital company, offers products to help customers plan for the future, protect the present, and achieve goals at every life stage. It is the largest insurer among independent companies, offering life insurance, pension plans, capitalization, and investments. With 38 branches nationwide and 8 million active deals, Icatu had an existing business intelligence structure. However, by 2020, due to increased information volume and a need for greater user autonomy, Icatu sought to elevate its solution to positively impact partners, brokers, customers, and internal areas.
Icatu Seguros recognized the opportunity to enhance its data processing and analytics capabilities to better understand customer needs and develop tailored insurance products. Traditional data processing methods were inefficient and time-consuming, limiting the company's ability to quickly derive actionable insights from their data.
"This first delivery evolved over the course of a year. We looked at our entire internal landscape and what we had from data sources and Microsoft supported us, analyzed the solution, everything we would need to do from data ingestion and pipeline and support from other consulting partners with follow-up and training"
“We deliver projects that have effectively given more power and autonomy for the business to evolve in driving its processes and results. Product, Marketing, Business Planning, Actuarial, and Controller have been our key internal customers. The team operates using the latest market tools, using agile methodologies." - Fabiana Ravanêda Vercezes: Data Intelligence and Analytics Team Coordinator - Icatu Seguros
To address these challenges, Icatu Seguros implemented Azure Databricks, leveraging its scalable and integrated data analytics platform. This allowed the company to perform advanced analytics, gain deeper insights into customer behavior, and develop more personalized insurance offerings. Key aspects of the solution included:
Data Extraction and Transformation: Adopting modern components such as Azure Databricks and Azure Synapse Analytics for ETL/ELT processes, and sandbox capabilities for engineers and data scientists to work autonomously and securely.
"We offer modern components for data extraction and transformation (ETL/ELT), such as Azure Databricks and Azure Synapse Analytics, and 'sandbox' capabilities, so our engineers and data scientists can work autonomously and securely, ensuring a balance between agility, security, and data privacy," - Fabiana Ravanêda Vercezes: Data Intelligence and Analytics Team Coordinator - Icatu Seguros
The implementation of Azure Databricks significantly improved Icatu Seguros' operations:
"From the moment we gain speed in making information available, it becomes possible to act much faster, focusing on more strategic deliveries with value generation," Fabiana Ravanêda Vercezes: Data Intelligence and Analytics Team Coordinator - Icatu Seguros
With both self-service BI and analytics, Icatu Seguros is laying the foundation for its digital transformation strategy, adopting AI and machine learning to build better processes, services, and products.
The implementation of Azure Databricks at Icatu Seguros offers valuable insights into how advanced technologies can drive innovation and efficiency in the insurance industry.
Here are the key takeaways from their experience:
The Icatu Seguros case study is available on the Microsoft website, and you can access it by clicking this link: https://customers.microsoft.com/en-us/story/1786906188811176218-icatu-seguros-azure-databricks-insurance-en-brazil
Voya Financial, one of the leading financial services firms in the United States, adopts Azure Databricks AI to enhance client engagement through real-time data analytics. This case study demonstrates the impact of advanced analytics on customer-centric financial services.
Fortune 500 financial services firm Voya Financial has long had a compelling elevator pitch: it aims to be “America's Retirement Company®.” Voya's name reflects the idea that retirement is a journey — or voyage — rather than a destination.
Listed on the New York Stock Exchange (NYSE: VOYA), Voya serves the financial needs of more than 14.8 million individual, workplace, and institutional clients across the U.S. In 2020, the company registered $7.6 billion in revenue and managed $721 billion in total assets as of June 30, 2021. Providing innovative health and wealth solutions to its millions of customers is at the heart of Voya's mission.
As digital technology continues to advance, Voya has been intentionally evolving its strategy to focus on becoming a technology-enabled company that provides holistic and personalized health, wealth, and investment management experiences.
Its strategy relies on technologies such as cloud computing, analytics, Big Data, machine learning, and artificial intelligence (AI) to make smarter investment decisions, analyze market trends, and better understand—and meet—its customers' needs. Along its journey, Voya developed an “Enterprise Data Science Team” dedicated to developing and applying AI algorithms and other technologies that will help its business units improve customer service and organizational performance.
As Voya advanced on its journey to the Azure cloud platform—to leverage more powerful and flexible computing power than traditional technologies stored on premises—Rejeesh and his team identified a use-case opportunity to support its investment management business. This use case involved Voya's investment administrative platform that manages approximately $252 billion in assets across fixed income, senior loans, equities, multi-asset strategies and solutions, private equity, and real assets.
“There was a clear opportunity to enhance the speed and flexibility of the computing tasks and the power of the administrative system itself,” explains Rejeesh Ramachandran, VP, Enterprise Data Science at Voya Financial.
To address these challenges, Voya Financial adopted Azure Databricks AI. This powerful platform enabled the company to enhance its data processing and analytics capabilities, allowing for real-time analysis of client data.
“Data is pivotal in understanding our customers. So we built a pipeline of AI and data science use cases within our business processes that will offer greater insight into customer behaviors.” - Rejeesh Ramachandran, VP of Enterprise Data Science at Voya Financial
Key components of the solution included:
Rejeesh says the firm considered expanding its on-premises platform to execute the new use case but decided a third-party service provider was a more sensible option.
“We don’t want to maintain a data science platform,” he says. “The data science team’s value does not come from technology expertise, so we don’t want to spend our time or be responsible for such tasks as patching servers. We want to be users of the product rather than managing the product.”
After talking to a range of IT suppliers, Voya did a proof of concept that included Azure Machine Learning, Azure Data Factory, and Azure Databricks. The team decided those were the components it needed to move forward and selected the Microsoft Azure cloud platform.
The integration of Azure Databricks AI had a significant impact on Voya Financial’s operations:
“The output of one such project can now be leveraged to enhance Voya Investment Management’s proprietary stock selection models, which will enable our quant team and fundamental analysts to make smarter investment decisions. By using AI, we are helping grow our customers’ assets.” - Rejeesh Ramachandran, VP of Enterprise Data Science at Voya Financial
Given this early success, the company expects to implement many more projects using the same technology to further improve customer service and investment returns. It's an example of how Voya is becoming a technology-enabled financial services company and bringing its strategy journey to life.
The Voya Financial case study is available on the Microsoft website, and you can access it by clicking this link: https://customers.microsoft.com/en-us/story/1422238195034103793-voya-banking-capital-markets-azure-en-united-states
While AI/ML technologies offer immense potential, financial institutions face several challenges in their implementation:
The future of AI/ML in finance is promising, with several trends and innovations expected to shape the industry:
The integration of Azure Databricks (AI) and Machine Learning has positively impacted SWIFT, Icatu Seguros, and Voya Financial. These organizations have enhanced security, improved operational efficiency, and they are now capable of providing personalized customer services in a more cost and time-efficient way. SWIFT's fraud detection, Icatu Seguros' customer insights, and Voya Financial's investment strategies all showcase the transformative potential of these technologies.
The success of these initiatives underscores the crucial role of AI and machine learning in the financial services industry. Get in touch with us and let's discuss how we can support your AI and machine learning developments as your Microsoft Gold Partner.
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