Maven: How Artificial Intelligence is Affecting Banking & Finance

Moreover, AI can guide applicants seamlessly through procedures, enhancing user experience. Large banks tackling knowledge management initiatives — sharing relevant information within an organization — often must grapple with siloed data, a problem worsened by legacy or outdated tech. Generative AI can help by processing vast amounts of data and promptly delivering information to those who need it. The ability to dynamically synthesize data means faster access to regulations for legal teams, product documentation for engineers, and  branding guidelines for marketers — all of which boost efficiency. However, the benefits of AI must be balanced against ethical concerns, data protection, and the possible impact on the workforce. As AI advances, it will transform the finance sector, opening up new opportunities and some unique problems for financial institutions worldwide.

AI enhances fraud protection in banking by analyzing previous transaction patterns to identify anomalies and alert the customer of possible fraud. Banks like Wells Fargo and Bank of America offer virtual assistants to provide customized financial advice, recommendations, and reminders to deepen customer engagement with their bank, thus forging lasting relationships. The future of AI in banking is full of promise and could lead to many further enhanced tools and services. AI’s ability to thwart identity theft attempts also includes alerting users of unusual login locations and spending patterns. This proactive approach to tackling fraudulent activity helps users feel more confident and safe with their bank of choice.

Risk assessment and credit scoring

This application allows financial institutions to alleviate the operational burden on staff software. For example, NLP can be employed to efficiently scan, process, and categorize physical documents, storing them securely in the cloud. Trading algorithms driven by AI can analyze market data, news, and historical trends in real-time, allowing for faster and more educated investment decisions. ML algorithms can identify profitable trading opportunities, optimize portfolios, and execute transactions at breakneck speeds, dramatically enhancing investor returns.

• Trading opportunities are taken advantage of, and deals are executed quickly, which are not achieved when done manually.
• HE allows these actions to occur within the vault, ensuring the interaction and corresponding results remain protected.
• Is leading the way in regulating AI, reaching a political agreement on December 9, 2023, on the EU AI Act, which is now subject to formal approval by the European Parliament and the European Council.
• Some of the most prevalent uses of AI in the finance sector are included below, along with how they continue to change the course and experience of financial services in terms of user experience.
• Their objective is to exploit any vulnerabilities within your system to gain access to this valuable data to commit financial fraud.

General counsel and compliance counsel need to be aware that there are new threats requiring modification of existing cyber-risk management strategies. That echoed the Executive Order, entitled “Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence,” which specifically calls out financial services, and requires the U.S. Treasury to issue a public report on best practices for financial institutions to manage AI-specific cybersecurity risks within 150 days of the Executive Order. Additionally, fraud detection systems should be constantly monitored for fairness, security, transparency and explainability. Issues identified should be corrected by the AI actors involved at the relevant lifecycle phase (including data collectors, developers, modellers, and system integrators and operators).

Trading Algorithms

Testers can use synthetic data to train and test fraud detection models, improve their accuracy and robustness, and expose their weaknesses. LeewayHertz specializes in tailoring generative AI solutions for financial companies of all sizes. We focus on innovation, providing personalized services, and enhancing competitive advantage through advanced risk assessment, fraud detection, and customer engagement applications.

After the integration of the DefenseStorm platform, employees were able to determine the scope of an event in one to five minutes to determine if the event needed to be escalated as a genuine incident. DefenseStorm claims to have integrated their SaaS analytics solution to upgrade Live Oak Bank’s existing data management and analytics systems over the course of a couple of months. After the integration, DefenseStorm claims that Live Oak Bank was able to optimize big data searching and saw a 50–60% improvement in their incident discovery. The bank was facing a challenge in aggregating all logs and event data (routers, firewalls, and intrusion prevention systems) into one dashboard where their IT security personnel could then easily search and manage incidents. In short, they needed to increase the visibility of security threats and reduce their reaction time to high risk, high-threat activities, without large-scale increases in headcount. In a case study with Live Oak bank, DefenseStorm claims the bank had many data centers around the US using multiple technologies and applications to support their small business lending and deposit platforms.

The Outlook for AI in Financial Services

The technology not only optimizes capital allocation but also reduces turnaround times through automation, streamlining risk assessment workflows without compromising accuracy. Generative AI also empowers financial institutions to analyze large volumes of financial data, trading volumes, and market indicators. It provides valuable insights that can inform investment decisions, risk management strategies, and fraud detection methods. By leveraging generative AI, financial services can gain a competitive edge by making data-driven decisions and staying ahead in the rapidly evolving financial landscape. From fraud detection to personalizing customer experiences and risk assessment, the successful utilization of Generative AI spans various applications in finance and banking.

Read more about Secure AI for Finance Organizations here.