As an industry, finance has always been walking a tightrope between innovation and regulation. The introduction of and continued enhancement of technology across finance has provided key benefits, optimizing operational efficiency, enhancing the implementation of investment strategies through data-driven insights, and the ability to monitor and identify activity in real-time.
As much as innovation has propelled finance forward, tension remains with the risk inherent with technology. When adopting any new technology, there must be full transparency and compliance controls.
Seemingly at odds, innovation and regulation can actually both be a priority without significant compromise to either. Achieving this balance depends on how tech firms implement technology in finance. The opportunities are broad, but they bring in new kinds of opportunities as well as risk. With AI, firms that want to embrace it should proceed with guardrails and parameters. Scrutiny will intensify, and firms must navigate an environment that requires oversight and adaptability.
Key Innovations Driving AI Adoption in Finance
AI in finance encompasses a diverse world of tools and breakthroughs. It’s already at widespread adoption, with 90% of global fintech companies already relying on the power of AI and machine learning.
Some of the most promising use cases reshaping financial operations support risk management, which regulatory agencies already require. AI is transforming investment technology by integrating approaches into portfolio management, trade execution, and downstream automation for middle and back office. Let’s explore some examples of this evolution.
Predictive Analytics for Investments and Risk Management
Data has become an essential asset for businesses. In investment management, the data is massive and comes from many sources. Only by aggregating and analyzing data as a whole can it be actionable for decision-making surrounding portfolio decisions and managing risk.
AI’s use case here is one of predictive analytics. The process involves using historical data, statistical modeling, and machine learning to help predict future outcomes with greater confidence.
In the predictive analytics framework, you start with the goal to achieve. For example, if portfolio performance is the objective, predictive analytics can detect trends that suggest market movements. For risk mitigation, the algorithms could focus on volatility signals or other historical data sets to make adjustments based on the goals of the investor.
Generative AI in Research and Compliance
Generative AI for data analytics is another innovation within finance that applies NLP (natural language processing) using large language models (LLMs) to analyze data more quickly and efficiently. This can assist in the areas of research and compliance.
Generative AI can scan data quickly and effectively to improve search tasks for research, allowing faster access to important information without it being time-consuming.
Generative AI is a good tool for analyzing investor documentation. For example, large language models use NLP for these dialogues, which can be more accurate than querying data.
On the compliance side, Generative AI using NLP enables:
- Automated document classification
- A simpler way to extract data and analyze it
- Measuring for compliance with regulations by identifying language in documents
- Automation of audit records
- Real-time updates and monitoring for rule changes
AI-Powered OMS/PMS Systems
AI is influencing the evolving landscape of the OMS/PMS market. A dual OMS/PMS incorporating practical AI streamlines workflows, boosting efficiency and speed in trade order management.
Its integration into these platforms is enabling:
- AI portfolio modeling for greater precision
- Automation to reduce clicks and keystrokes in workflows and trade creation
- Big data analysis to identify patterns and influence decision-making
Regulatory Complexity in the Age of AI
AI regulation in finance is still in flux. AI use does not usually fall under existing guidelines. Instead, AI implementation echoes existing controls and policies for other reasons, including data security and privacy, governance, and risk management.
The areas of emerging guidance include:
- Governance frameworks that specify the role of human intervention to prevent harmful outcomes
- Model risk management for the explainability of AI
- Data governance and management
AI also brings about new legal questions. These have to do with data usage and content generation. Regulations also seek to counter the increased risk that any technology presents. There are regulations regarding AI in place, including:
- The EU AI Act: This law supports transparency in AI use and defines different rules for different risks.
- U.S. Securities and Exchange Commission (SEC) guidance: The agency recently hosted a roundtable, asking for feedback on AI’s risks, benefits, and governance. Experts deemed it a “reset” to the organization’s approach. Some key points were avoiding unnecessary barriers, the potential for fraud with generative AI, keeping “human-in-the-loop” guardrails, and using risk-based control structures.
Other global privacy laws also cover some parts of AI, including GDPR. On the horizon are potential U.S. state laws to govern its use.
In considering what’s already a regulation and the emergence of new ones, it’s clear that transparency is critical. The lack of transparency in AI models complicates compliance and fiduciary trust.
Transparency and Explainability: A Non-Negotiable Standard
Evaluating AI solutions for investment management requires heeding regulations and guidance already in place and what’s to come. Firms must scrutinize how platforms enable transparency surrounding technology, methods used, and possible outcomes.
There are several topics to discuss here. First is the growing demand for explainable AI (XAI). It represents the requirement for AI decision-making processes to be transparent and understandable to humans. In other words, it seeks to transform opaque black-box AI into one that’s clear, minimizing operational and reputational risks. XAI is vital to ensuring fairness and accountability, rooting out bias, and building trust for its conclusions.
Transparency should be the default in model validation, audit trails, and data lineage. If vendors cannot explain AI’s technical background, it’s a red flag. Typically, this means there are no mechanisms in place for transparency.
Building Responsible AI Systems in Practice
By evaluating the challenges and opportunities for AI in finance, firms should employ best practices when using these systems. These include:
- Adopting open architecture platforms, which enable smoother integrations and flexibility for compliance.
- Performing regular audits and bias mitigation strategies.
- Providing human-in-the-loop frameworks to oversee critical decision points.
At a foundational level, AI should augment human decision-making. It does not replace it and cannot be a sole source for making portfolio management, trading and compliance decisions.
Balancing Innovation with Governance: A Strategic Imperative
When introducing new technology into any industry or business, leaders should define governance frameworks from the start. This is especially critical with AI because of its complexities and effect on decision-making.
If you wait until later to try to define guardrails, it becomes a moving target, making it hard to be confident in the capabilities of the AI and its outcomes. Clearly, this is a big investment for firms in terms of money and time, so beginning with governance ensures it doesn’t become something that snowballs out of control.
So, what does governance look like in this scenario? Much of this concerns the technology you implement and how well its controls have been designed. A system should also be cloud-native and interoperable, which provides both innovation and the safeguards needed to make AI a reliable and practical tool.
How you use AI also sets up much of what you need in terms of governance. For example, you’ll need much tighter restrictions and oversight for AI-automated trading versus simpler workflow automation.
Collaboration of stakeholders must be a priority as well. This group includes firms, technology providers, and clients. Only when all parties have an open dialogue, goals, and defined governance can innovation bloom.
The Future of Effective AI in Finance
AI provides broad use cases and the power to drive efficiency, manage data, and improve decision-making. However, it’s not something to integrate into finance without caution and purpose. There are too many pitfalls that can arise, as well as compliance considerations.
Innovation and transparency can coexist, and neither needs to stifle the other. When they run in parallel, investment firms can be confident that their use cases for AI meet operational and compliance requirements. Those who can achieve this balance will be tech leaders and stewards of trust in financial services.
The true potential of AI lies in its responsible evolution. Audits should be common, and bias should be rooted out, with the framework that AI augments human intelligence, not a replacement for it. Anyone in the industry who wants to capitalize on AI must balance it with transparency and trust.
