Financial Services Industry Brief

In 2017, Forbes asked the question: “Why are banks — who are typically the most capable and tech-intensive players in the business world — acting like Luddites with AI?” [1]

Since that question was posed a lot has changed, both in artificial intelligence, and in AI’s acceptance in the financial services industry. Financial institutions have moved fast and furiously towards AI, realizing that it is a key technology driving digital transformation and enhancing customer experience. AI can create differentiated value, combat financial crime, increase efficiencies, and reduce costs, as well as provide new levels of security.

The data revolution in financial services

As AI continues to grab headlines across the financial sector, new use cases develop, leading to increased adoption by financial institutions. Business leaders recognize that business intelligence, advanced analytics, and artificial intelligence deliver significant capabilities but rely on large volumes of high-quality data to produce high-quality results. In other words, enterprise-wide analytics and AI services require scalable, reliable, and high-performance data pipeline, as well as the democratization of data.

At a transactional level, one common use case is smart bots that provide customers with self-help solutions, easing the call center workload. Towards Data Science identifies a dramatic improvement in loan processing, seeing “a faster, more accurate assessment of a potential borrower, at less cost, that accounts for a wider variety of factors, which leads to a better-informed, data-backed decision. Credit scoring provided by AI is based on more complex and sophisticated rules compared to those used in traditional credit scoring systems. It helps lenders distinguish between high default risk applicants and those who are credit-worthy but lack an extensive credit history.” [2]

Big data use cases in financial services

Beyond loans and improved customer experience, big data and AI in all its forms are transforming financial services and these five major use cases are just the beginning: [3]

• Compliance: Creates automated intelligent reporting for financial auditors; enables Anti Money Laundering, Know Your Customer, and stress testing.
• Risk Management: Optimizes credit risk evaluation, loss scenarios, and standard deviation of financial portfolios for trading and enterprise risk management.
• Wealth Management: Allows for personally tailored, real-time management advisors with virtual assistants.
• Security: Improves cyber security (based on machine learning for anomaly), fraud detection, malware, data leakage, and insider trading detection.
• IT Operation: Enables infrastructure anomaly detection and performance optimization using predicative analytics and machine learning, as well as application development and security vulnerability detection.

Navigating the AI pipeline

To capitalize on any of these benefits, financial organizations need to take a hard look at their data. Deloitte notes that a fragmented approach will not work: “Rather than taking a siloed approach and having to reinvent the wheel with each new AI initiative, financial services executives should consider deploying AI tools systematically across their organizations, encompassing every business process and function.” [4] 

This means organizations should scale and integrate multiple data pipelines which is not an easy task. A single organization may need hundreds of data pipelines. While this can be cost-prohibitive, the right data architecture structure along with capable storage, networking, and compute technologies can harmonize multiple pipelines. The end result is a high-performance and scalable infrastructure that delivers data for advanced analytics and deep learning for all forms of AI projects.

In each of these pipelines, the data dramatically changes in volume, velocity, and variety, depending on the type of use cases. To navigate it, you need a partner—and a solution—that understands and solves for end-to-end data pipeline and AI project phases.

Solidigm and financial services

Your data architecture defines data flow from discovery to storing, processing, and delivery. This is primarily driven by application architecture but relies on underlying storage architecture in memory or on persistent storage, such as a solid-state drive (SSD). And when it comes to supporting multiple AI projects with different types of workload demands in financial services, Solidigm provides the performance and capacity needed to meet those different needs.

The Solidigm D7-P5810, a single-level cell (SLC) NVM Express (NVMe) drive, delivers a combination of performance and capacity that is ideal for extremely write-heavy workloads, such as high-frequency trading, caching, and databases and, along with Solidigm CSAL technology, provides the high-performance and high-capacity storage needed to access, move, and store large datasets through the required phases of the data pipeline.

Throughout different phases of the pipeline, Solidigm technology optimizes storage resources across a wide variety of workloads, access patterns, and block sizes. PCIe-based SSDs, such as the D7-P5810, unlock the value of stored data while reducing storage space and cost.

Learn more from the Solidigm D7-P5810 Product Brief to see how you can leverage large datasets for AI use in the financial services industry.