4: AI IN FINANCE & RESPONSIBLE ai
Finance is a department that is prone to a ton of manual processes and promises to benefit from the field of Artificial Intelligence (AI) and its advancements immensely. Pitchbook recently released a report that stated that the volume of private deals made in the “CFO stack” category superseded the “Payments” vertical for Q1’25 and over the TTM period. This is significant given Payments is a large field encompassing domestic and cross border commerce, money movement, fraud and a whole lot more trade linked sub-categories, while the CFO stack applies to Finance teams and directly benefits internal users.
This article explores the subsets of AI, its use cases as they apply to the CFO stack, and an area called Responsible AI.
Subsets of AI
Source: community.aws
AI is the total subject area and its subsets are NLP, DL and ML that are not new, although Conversational AI and LLM are the most popular and are ones in the spotlight recently. Generative AI is the application of LLMs that can produce text, speech, image and video, that organizations often refer to as “multi-modal”.
Moving on to now exploring solutions and use cases within AI in Finance
Subsets of AI
Machine Learning
A type of artificial intelligence (AI) that allows computers to learn from data and improve their performance without being explicitly programmed for every specific task
Deep Learning
A specialized subset of Machine Learning that uses artificial neural networks with many layers to model and solve complex problems—especially those involving unstructured data like images, audio, text, or video.
Natural Language Processing
A branch of artificial intelligence that focuses on enabling computers to understand, interpret, generate, and respond to human language.
Conversational AI
A type of artificial intelligence that enables computers to engage in human-like dialogue, either via text or voice. It powers tools like chatbots, virtual assistants, and interactive voice response (IVR) systems.
LLM
A Large Language Model, is a type of artificial intelligence model trained on vast amounts of text data to understand, generate, and interact using human language.
The table below lists common Finance processes and the AI technology that can service it. Manual processes are a thing of the past.
Transforming your organization into a digital and AI-ready organization requires adopting an enterprise wide approach to developing a finance technology roadmap so you maximize the benefits of AI that begin with identifying opportunities in the organization’s current portfolio and ending with developing a trackable technology roadmap.
A thoughtful finance technology roadmap development avoids common pitfalls like adopting technology based on hype and identifying opportunities in silos.
It is natural for users to feel nervous about adopting AI. Are we in compliance with the law? What if something goes terribly wrong or data gets compromised?
Well, with regulations around AI still evolving in the US and around the world, organizations are focusing on using and promoting AI responsibly instead.
Responsible AI
Any technology that promotes unfair or deceptive practices such as biased models or misleading outputs would be classified under using AI irresponsibly.
In the US, the FTC is the most active body engaged in the monitoring of Responsible AI.
The core principles of Responsible AI are:
Fairness: should avoid any discrimination based on race, gender or age
Transparency: AI systems should clearly explainable and understood
Accountability: Makes human responsible for AI decision and impacts
Governance: Protects user data and applies strong data protection practices
Safety and Security: Ensures that AI systems are robust against failure, misuse
Human Oversight: Keeps humans in the loop for critical decision making
Inclusiveness: An extension of “Fairness” that the system does not exclude
Sustainability: Minimizes energy use especially from LLMs
So, what does a user need to know before using an AI system
The purchasing party will need to make sure that the AI system:
Is transparent and the purchasing party understands what the system is meant to be doing.
Performs as expected, is devoid of recurring errors, improves itself and has been tested on real world data.
Has been audited for bias and fairness and that it gives the user the option to override results.
Protects user and related data.
Promotes accountability and has human oversight in critical decision making.
Does not inflict harm and is protected from any manipulation or adversarial attacks.
Understand and follow the law i.e. HIPAA, FDA, FTC
Leaders vs the Rest
Only a handful of finance functions (30% per Gartner) are leading AI finance organizations and the one thing that separates the leaders from the non-leaders is leaders have an external talent hiring strategy versus attempting to upskill their staff.
Source: 4 AI Implementation Lessons from Leading Organizations, Gartner Finance
In conclusion, AI is all around us and the CFO stack is ripe with financial transformational use cases.
A trusted consultant can help you navigate to your ultimate goalpost of designing a robust financial technology roadmap and implementing cutting edge solutions.
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