Navigating the complex terrain of AI in finance requires more than just theoretical knowledge; it demands real-world expertise. This article delves into the pivotal lessons gathered from industry leaders who have successfully integrated artificial intelligence into their financial strategies. Discover the critical importance of balancing innovation with practicality, as explained by those at the forefront of this technological revolution.
- Embrace AI Early, Maintain Human Element
- Leverage AI for Competitive Advantage
- Prioritize Data Quality and Specificity
- Solve Real Problems with Targeted AI
- Use AI Early and Responsibly
Embrace AI Early, Maintain Human Element
If I could give my younger self one piece of advice about using AI in finance, it would be: embrace it early, but never lose sight of the human element. As a Finance Partner in the US, I’ve seen how AI can transform the industry, from streamlining loan approvals to enhancing risk assessments. However, I’ve also learned that AI is most effective when it complements, rather than replaces, human expertise and judgment.
When I first encountered AI in finance, I underestimated its potential to uncover patterns and insights that traditional methods couldn’t. For instance, we’ve successfully integrated AI tools to assess borrower risk more efficiently by analyzing vast datasets, such as market trends and credit histories. This has allowed us to make quicker, data-driven decisions while maintaining the flexibility to customize solutions for each client.
What I’ve learned along the way is that AI isn’t a shortcut; it’s a tool that amplifies the value of experience and intuition. For younger professionals, my advice would be to not only understand how AI works but also to remain critical of its outputs. Technology can analyze data, but it’s the human touch that ensures decisions are ethical, empathetic, and aligned with client goals.
In finance, particularly in private lending, relationships matter as much as numbers. AI can enhance our efficiency and accuracy, but trust and understanding come from personal connection. The key is striking the right balance between innovation and humanity, using AI to empower, not replace, your ability to deliver value to clients.
Kalpi Prasad
Finance Partner, Renown Lending
Leverage AI for Competitive Advantage
Many have heard the saying: AI won’t replace your job, but someone who knows AI will.
In finance and many other industries, AI cannot do everything in our job–as of now, Nvidia’s CEO Jensen Huang estimated that AI can do around 20 to 50% of our job 1000x better and the real threat of AI is that the person who uses AI to automate that 20% is going to take our jobs.
MIT researchers have told us only 23% of workers’ compensation as “exposed” to AI computer vision would be cost-effectively replaced by AI systems. As AI hallucinates and the output is not always accurate, human intervention and evaluation are critical when using AI. Therefore, humans are indispensable.
Have no fear about AI but learn everything we can about it so we can make AI our best intern and not our master.
For example, leverage LLM models to help us summarize whitepapers and research and create tables and graphs so we can spend our time on our clients, business development, or business strategy. Use LLM to help us write marketing messages and devise marketing campaigns, or ask it to help us better craft messages for particular audiences.
As we learn to work with AI, we can do our jobs better and more effectively and create new opportunities for ourselves.
Marianne O
Co-Founder and Portfolio Manager, Lumen Global Investments
Prioritize Data Quality and Specificity
If I could give my younger self advice about using AI in finance, it would be this: start with a clear problem to solve and ensure your data is clean and reliable. Early on, I underestimated how crucial data quality and specificity are for successful AI implementation. For example, in a project to predict cash flow trends, we fed the AI model with inconsistent and incomplete financial data, which led to inaccurate outputs and misguided strategies.
What I’ve learned is that AI is only as good as the data it processes. Spend time cleaning and structuring your data, and ensure it aligns with the specific financial questions you’re trying to answer. Also, focus on explainable AI—understand the “why” behind your model’s predictions to make smarter, more informed decisions. By combining precise goals, high-quality data, and interpretability, you can unlock AI’s real potential in finance.
Marouen Zelleg
Co-Founder, Crestal
Solve Real Problems with Targeted AI
If I could give my younger self one piece of advice about using AI in finance—or any field—it would be this: focus on solving real problems first. AI is not a magic solution; it’s a tool that’s only as effective as the problem it’s designed to address. In finance, this might mean automating routine tasks or analyzing patterns to make better decisions. We applied this principle to create tools that help small restaurants optimize operations, bridging the gap with larger chains.
What I’ve learned along the way is that the most successful AI projects start small and grow with their successes. Some of the most impactful innovations in finance—like fraud detection or credit scoring—are the result of carefully targeted applications of AI. Similarly, we use AI to tackle specific challenges, such as menu optimization or staffing predictions, rather than attempting to overhaul entire systems at once.
The universal lesson is to start simple, stay practical, and let data and results guide your next steps. Whether in finance or restaurant technology, AI’s true value lies in empowering people to make smarter, faster decisions, not in replacing them.
Manoj Kumar
Founder and CEO, Orderific
Use AI Early and Responsibly
If there’s one important piece of advice for using AI in finance, it’s to start using it early but always focus on doing it responsibly. AI is changing the financial world by making tasks faster, improving decisions, and finding valid patterns in large amounts of data. By 2027, the global AI market in finance is expected to grow to $130 billion. Using AI can make decisions more innovative and more efficient.
However, success with AI depends on using clean, accurate, and fair data. Poor-quality data can lead to financial mistakes. Ethical use of AI is also essential. If not managed carefully, AI systems can create unfair biases or be unclear about how they make decisions, which can damage trust. The best way to make use of AI is to keep learning and staying updated as technology advances. With the right approach, AI can help finance professionals work smarter and deliver better results.
Ethan Richardson
Financial Consultant, Exquisite Timepieces
