Importance of AI in Banking + Financial Services
Delivering a positive client experience is the key to unlocking success across the global financial sector. To put it simply, banks perform well when their customers do. In fact, a recent McKinsey study found higher degrees of customer satisfaction drive higher assets under custody.1
As financial institutions continue to embrace the next generation of technology, they are looking to real-time and tailored services, transparency and speed of information as well as a high level of security. Artificial intelligence (AI) has applications across the spectrum of these functions and enables banks to deliver optimal experiences for their clients. In successfully unlocking these capabilities, McKinsey estimates that AI technologies could potentially deliver up to $1 trillion of additional value for banks each year.2
Evisort: A Case Study in AI Application in Banking
Originally, with each new custodial agreement, BNY Mellon tasked a team of lawyers to manually create a new contract based on approved, standardized agreement language and navigate a complex web of internal approvals and escalations. Contracts were sent by email through the approval process, with each lawyer individually reviewing prior changes, making new updates and emailing the updated contract to the next reviewer.
This legacy process repeated itself until the agreement was finalized but came with a potential for gaps in accountability, version control and progress tracking. As a contract was finally executed, multiple review and summary forms then had to be compiled about the details of the agreement. This created additional time to the process contracts to ensure all documentation and compliance was completed. This led to prolonged onboarding periods for the client, resulting in increased costs.
BNY Mellon sought to automate the custodial agreement generation process to simplify stages of the review process with AI. We collaborated with Evisort, an AI company backed by Microsoft’s M12, that leverages ML and NLP to streamline contract generation, review and management. Evisort customized its artificial intelligence tools to review new custodial agreements based on BNY Mellon’s internal rules, guideline and processes.
Evisort then used its AI technology to automatically create customized initial contracts and digitally coordinate with the necessary internal stakeholders for approval of special terms. Furthermore, Evisort's technology can automatically flag non-standard language and alert the necessary legal team members as well as automate the decision-making process, allowing attorneys to focus on more strategic tasks. For example, for all custody agreements, an attorney needs to fill out a detailed, multi- section worksheet to determine the most appropriate BNY Mellon legal entity to enter into a global custody or depositary agreement with a client. Evisort automates this decision-making process through a simple intake form.
In incorporating Evisort’s capabilities, BNY Mellon can now create, tailor, update and track contracts in a fraction of the time. In automating manual tasks, BNY Mellon is able to shorten and help streamline the onboarding process and effectively drive a better client end-experience.
In leveraging AI, BNY Mellon will continue to improve contracting efficiency and established the timely and appropriate escalation of deviations from standard contract language. The datatization of agreements additionally enables the bank to develop an instant "best first draft" that is tailored to each client, decreasing the amount of back and forth necessary in contract negotiations.
Embracing the future of AI, BNY Mellon plans to leverage both NLP and ML to monitor risk exposure in contracts by using AI to identify nonstandard contract language, creating an alert system for ongoing obligations, unsigned contracts and/or upcoming renewal dates, as well as continuous monitoring of compliance as regulations change. In addition, AI will allow the bank to better understand common deviations and educate relationship managers on when those deviations are appropriate and best course of action for proper mitigation.
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Identifying & Understanding the Rewards of AI
While the use of AI in the financial services industry is still in its early stages, the technology in combination with traditional data sources and has the potential to provide invaluable insights to inform and accelerate human decision-making and enterprise-wide growth.
Harnessing the power of AI, banks and financial institutions can:
Create and Leverage Better Integrated Datasets
Large global financial institutions have traditionally struggled to access and integrate data siloed across various teams and systems. As organizations migrate their data from “on-premise” locations into the cloud, technology managers are seeking data management platforms that can:
- Leverage machine learning to provide greater flexibility and agility across data types as volumes grow
- Deliver quicker ways to onboard new data to create actionable fit-for-purpose data models
- Provide data quality management capabilities to easily access information
AI has the ability to solve these challenges. For example, BNY Mellon’s Data Vault, a data management platform exemplifying these principles, leverages machine learning (ML)across disparate datasets to identify trends and patterns in the data to unearth insights. The analysis provided can help client service teams better inform their clients about strategies that may enable better financial decisions. It can also be applied to internal processes, helping managers to operate more efficiently.
Improve The Customer Experience
Financial institutions are eager to use AI’s enhanced ability to provide more tailored customer experiences, transparency into transactions and 24/7 client support across the globe. Conversational agents and chatbots provide the opportunity to supplement support teams in order to enhance client service. These support services create a pathway to build stronger relationships, bolstering reputational outcomes, increasing referrals for new business and driving client retention
Increase Operational Efficacy
Certain AI technologies such as robotic process automation (RPA) and low-code platforms allow teams to automate manual and repetitive tasks. This capability can reduce risk intrinsic in manual processes, creating greater efficiency and opportunities for employees to take on more sophisticated tasks.
Enhance Risk Management
AI-powered systems can use machine learning across various datasets to identify risk signals such as insolvency or fraud. Similarly, natural language processing (NLP) can perform sentiment analysis to help a bank identify customers who show signals of dissatisfaction that correlate to a risk of client churn. Accurately anticipating these risks can help client teams make better decisions when issuing loans or knowing with whom they should prioritize engagement.
Improve Data Security
ABI Research estimates that the financial sector will spend $96 billion on AI and cybersecurity analytics by the end of 2021.3 As data and cybersecurity become increasingly important, AI-based systems can provide signals that are an effective mechanism against malefactors. The programs analyze customer behavior, location and financial habits to trigger a security mechanism that will detect any unusual activity. The use of AI security systems could present additional ways to help keep client data even safer.
Bolster Regulatory Compliance
AII provides the potential for financial institutions to navigate complex and changing regulatory guidelines with greater sophistication and efficiency by analyzing data and automating manual compliance processes. These capabilities presents tremendous opportunities, as 51% of financial institutions currently report manually managing key regulatory protocols including “Know Your Customer” (KYC) and “Anti-Money Laundering” (AML).4 Leveraging AI can help drive efficiencies around these processes, enabling banks and other financial institutions to perform faster and-more in-depth screening of enterprises and conduct financial crime discovery and analytics to optimize workflows.
AI in Financial Services in the Future
AI is poised to transform the global financial ecosystem. More than 80% of financial services providers believe that AI is important to the future of their organization and one-third believe the technology will increase company revenue by more than 20%.7
“Financial services institutions are leveraging AI to transform business processes, differentiate their customer experience and innovate new products,” said Luke Thomas, Head of Banking at Microsoft. “Microsoft Azure AI Cognitive Services and Azure Machine Learning are empowering financial services institutions to develop artificial intelligence solutions for complex processes—such as predicting securities pricing, automating client service requests and processing financial statements—all on the trusted, secure and compliant Azure platform. AI is, and will continue to power the transformation and future of the financial services industry.”
As AI technology continues to gain momentum, organizations will do well to not only look to new capabilities, but to prepare current legacy infrastructures with dynamic fintech and technology partners.
This sentiment is underscored by Mark Casady, Founder and General Partner of Vestigo Ventures and a member of the BNY Mellon Accelerator Program’s Venture Capital Advisory Board.
“AI is the present and the future of financial services,” said Casady. “This is due to a combination of talented engineers coming out of great universities well trained in AI along with easily available data from incumbents and a desire to drive down costs among key players in financial services.”
“Incumbents in financial services should have their data warehouses and data lakes ready to be accessed. This is a critical step to getting the power of an AI technology that can harness that data to drive efficiency and a better outcome for customers.”
7 AI is Enabling Digital Transformation Across the Financial Services Industry. Finextra. July 29, 2021.