AI’s Role in Financial Institutions
The financial services industry (FSI) is highly competitive and subject to stringent industry regulations. These industry dynamics strongly influence how technology is adopted within the industry and require financial institutions to continuously identify new opportunities to differentiate their capabilities using technology.
These dynamics have created a ripe opportunity for artificial intelligence, a powerful technology that enables computers to predict future outcomes by leveraging historical data sets to increase efficiency and enable new customer experiences.
As a result, most financial services executives expect artificial intelligence to become a pivotal element of success within the next few years. According to a 2021 survey by NTT DATA Services:
- 83 percent [of financial services executives] agree that AI is creating new ways to differentiate offerings and win customers, driven by access to unique data sets.1
- 81 percent said that AI is a critical part of their strategy to attract and retain customers.1
The increased application of AI in financial services enables financial institutions to streamline core business processes while adding innovative products and services that improve customers’ experiences.
AI in Banking
In the banking industry, artificial intelligence helps companies to automate business-critical processes such as risk management and fraud prevention while unlocking new capabilities, such as the use of chatbots and intelligent recommender systems for retail banks.
Fighting Financial Crime
Banks are bound by a complex set of laws and programs that are designed to uncover the financing of criminal activities, both domestically and internationally. For example, the International Monetary Fund, as well as the US and other countries, have established anti-money laundering (AML) regulations, requiring financial institutions to maintain AML programs and report suspicious activities.2
While many of these regulations have proven expensive for banks, the rules have been largely ineffective at preventing or deterring financial crime. Legacy hardware has created a barrier to success as older systems lack the scale to combat threats and manage complex databases across various business units. Further, AML measures increasingly require real-time analysis to enable faster transactions or support online capabilities.
As a result, companies are turning to artificial intelligence to navigate industry regulation and increase efficiency through real time analysis. This is best demonstrated by PayPal who improved the detection of fraudulent transactions using Intel® technologies integrated into a real-time data platform from Aerospike. Key results included a 30x reduction in the number of missed fraud transactions with a 3x reduction in hardware cost.
The Connected Branch
In addition to modernizing traditional processes, artificial intelligence can be used to deliver enhanced customer experiences through new services and capabilities. In retail banking, the latest technologies enable banks to understand customers’ needs and offer personalized banking services that are tailored to each individual.
Inside the branch, AI-enabled machine vision solutions help to bridge the gap between the physical space and digital channels, including on-site kiosks. For example, machine vision‒based sensors can track customers’ gaze, posture, and gestures; assess wait times; and alert bank employees when a customer needs assistance. These AI-enabled solutions analyze behavioral data from the branch and from online channels. The resulting intelligence is used to individualize and optimize purchasing, placement, and timing of marketing displays and campaigns.3
AI in Capital Markets
Artificial intelligence is also being used by financial institutions operating in capital markets—asset managers and hedge funds, among others—to improve efficiency and deploy new capabilities. The technology is often used to support risk management processes in addition to optimizing trading strategies for a variety of financial instruments.
Liquidity and Risk Management in Trading
AI can help investment banks and other financial institutions to comply with a new set of international regulations called Fundamental Review of the Trading Book (FRTB). Beginning in January 2023, financial institutions will need to calculate all risks associated with their trading positions in securities, commodities, foreign currencies, and other investments. Because of the massive scale, FRTB compliance will rely on complex financial modeling, simulations, and impact studies that require enormous investments in computational power and data storage capacity.4
Artificial intelligence can be used to significantly increase the speed at which this analysis is completed. For example, software vendors such as Matlogica and Quantifi achieved significant performance improvements through a variety of valuation adjustment (xVA) models based on machine learning and deep neural networks. These AI-enabled enhancements help capital markets companies to remain compliant while significantly improving the efficiency of their risk models.
Within capital markets, AI is also enabling new capabilities, including the real-time analysis that supports algorithmic trading. Financial trading is based on patterns that are revealed in a history of market behavior and transactions. Recently, companies have begun using AI capabilities to deploy algorithmic trading which relies on machine learning, neural networks, and predictive analytics to interpret and respond to market signals within microseconds.
According to a 2020 JPMorgan study, over 60 percent of trades over USD 10 million were executed using algorithms. The algorithmic trading market is expected to grow by USD 4 billion by 2024, bringing the total volume to USD 19 billion.5
While algorithmic trading is not new, today’s AI capability accelerates the near-real-time analysis needed for traders to remain competitive. This is best demonstrated by solutions from key Intel partners, Aerospike and MemVerge, that leverage 2nd Generation Intel® Optane™ technology to enable the real-time storage and analysis that is required in the trading industry.
AI in Insurance and Payments
Finally, artificial intelligence is being used by insurance and payments companies to automate processes, improve efficiency, and deploy new capabilities.
Underwriting and Claims Management
Within the insurance industry, companies are deploying predictive models to streamline the underwriting and claims management process with artificial intelligence.
During customer onboarding, insurers can assess an applicant’s risk factors at a given time. These increasingly sophisticated models rely on machine learning to analyze a variety of factors (e.g., credit, health) to offer a customized premium for their insurance services. Once a customer is onboarded, insurance companies are using AI to receive and process insurance claims with high performance and accuracy. This enables customers to receive insurance services quickly and efficiently. These processes are enabled by robotic process automation technology, which is a machine learning technique that enables hyper automation of various tasks.
Payment processors and credit card issuers also deploy a recommendation engine to predict the preferences of customers and prospects. The institutions then offer personalized banking services to those prospects whose demographic profile and behavior either follow a discernable pattern of their own or resemble a similar group whose behaviors are known.6
The machine learning‒based recommendation engine analyzes vast amounts of preference data to choose the best fit between product and prospect. These engines are similar to those used in e-commerce stores or streaming media services that recommend additional items based on an individual’s past purchases and on related purchases by other customers with a similar history.
Financial Services Technology
Regardless of the use case, Intel is the vendor of choice to support companies in their artificial intelligence journey. Intel helps financial institutions deploy AI through hardware-enabled AI acceleration, ecosystem optimizations with key partners, and hands-on customer support.
Firstly, Intel offers companies flexibility with a portfolio of products aimed at accelerating their artificial intelligence deployment. The 3rd Generation Intel® Xeon® Scalable processor is an ideal platform for a wide array of AI usages and the only x86 datacenter CPU with built-in AI acceleration. Key features such as Intel® Deep Learning Boost (Intel® DL Boost), Intel® Advanced Vector Extensions 512 (Intel® AVX-512), and Intel® Software Guard Extensions (Intel® SGX) deliver significant acceleration of AI workloads and encrypted data. Other key Intel® products such as 2nd Generation Intel® Optane™ persistent memory and Intel® Iris® Xe GPU enable real-time data analysis and computation for dedicated training workloads.
While Intel is best known for its hardware, the company also invests significantly in software tools, libraries, and partners to enable seamless adoption of artificial intelligence. The 3rd Generation Intel® Xeon® Scalable processor is optimized for the most-popular data science tools and libraries, enabling practitioners to build and deploy their own AI solutions. Optimizations with BigDL, TensorFlow, PyTorch, scikit-learn, and the Intel® Distribution of OpenVINO™ toolkit enable developers to scale their AI environments seamlessly across nodes from edge to cloud.
Intel® technology is also optimized with the largest cloud providers and hundreds of commercial software vendors and the company continues to participate actively in the open source community, including the Linux Foundation and FinOS. These efforts have resulted in a broad array of partner solutions that help financial institutions accelerate their AI performance and improve their time to business value.
Finally, Intel has been working with financial services companies for decades to help them address their most complex challenges. As a leading technology innovator, Intel serves as a trusted partner to financial services institutions that are interested in deploying artificial intelligence within their organizations. This experience is critical to ensuring that the financial services industry has the tools and resources it needs to compete globally.