Executive Summary:  We will walk through up-to-date ways FinTech CFOs can utilize the current strengths of AI to improve their company’s operational effectiveness.

Introduction

The FinTech industry is grappling with unpredictable revenue streams, shorter funding cycles, and the pressure to scale quickly with leaner teams. Since our last update, AI has continued to improve its ability to provide powerful solutions to these challenges by automating operations, improving accuracy, and ensuring compliance. McKinsey notes that AI boosts efficiency by 20% and nearly 72% of companies are already using AI. FinTech companies that integrate AI into their core operational procedures will empower themselves to scale more quickly with less risk.  

The FinTech industry encompasses a wide variety of sectors (payment providers, lending platforms, expense management, marketplaces, investment apps, and more), but all share similar complexities in data, compliance, and risk. AI’s ability to analyze large amounts of data in real-time is particularly well-suited to these tasks, allowing FinTechs to:

• Scale without headcount growth: AI allows individuals to automate most routine tasks and level up their expertise, increasing the team’s ability to operate efficiently without increasing staff size.

• Automate flags for overextended CFOs: Set up agents that act as real-time monitors of all financial and operational data to point out performance flags before they become issues.

• Respond quickly and transparently: Adapt pricing and strategy in response to macroeconomic conditions and keep investors informed.

Why the CFO:

CFOs are in the best position to push these measures forward, combining their expertise in data and operations with their company-wide mandate to improve resource allocation.  Depending on the use case, this could involve: 

• Prioritizing development of in-house models that run on real-time transactions

• Buying and integrating domain-specific off-the-shelf AI tools

• Using popular LLMs (e.g., GPT, Anthropic) which allow for large context windows to upload hundreds of documents and thousands of data points

Effective use of these AI has a significant impact on managing risks, driving efficiency, improving profitability – all primary responsibilities of CFOs.  Furthermore, they have deep insights into the business metrics and budgets that will ensure AI aligns with the overall business goals and deliverables measurable ROI.

How to Empower your Company

An effective AI strategy involves having centralized data to use and shifting the culture to asking “Can I do this faster with AI?”  While initial skepticism may arise, the eight sections below show that the benefits are clear. Companies are already using AI to enhance performance, and it will be crucial for staying competitive going forward. To successfully implement in your organization, consider the following approach:

• Implement these tools within your own team first by evaluating the current biggest ‘time suck.’  Then task the team to experiment with popular LLMs before experimenting with buying off-the-shelf tools.

• Provide a clear case study to the organization for how it saved the team time and allowed them to focus on less ‘manual entry tasks’

• Clearly discuss the mandate in the management team to ensure the buy-in.

• Roll out through other parts of the organization, by utilizing on champions (team members excited for AI) and finance business partners to solve the most impactful problems

Even if AI is only implemented within your own team, you should see significant gains in efficiency, as finance teams still handle a lot of manual data entry compared to other departments.

Using AI Now:

Scale Without Headcount Growth

1. Automate the FinTech Back Office

Back-office tasks like cash flow forecasting, reconciliations, and financial reporting are especially time-consuming in FinTech’s high-data, high-complexity environments. AI provides a solution by automating these processes, freeing teams to focus on strategic initiatives.

• Reconciliation automation: Match transactions by date, description, and amount to vendors and suppliers (e.g., Xero).

• Loan disbursement: Analyze thousands of data points to assess credit risk and approve loans (e.g., Kabbage).

• Fraud risk: Escalate abnormal transactions for manual review (e.g., Stripe).

• Transaction tagging: Automate data tagging with rule-based descriptions and re-tag old descriptions using the same method (e.g., Divvy).

• Expense management: Flag transactions that exceed set amounts or deviate from trends (e.g., Brex).

• Cash flow forecasting: Forecast short-term cash flow by analyzing spending, collections, and trends by seasonality and growth forecasts (e.g., Travolta).

• Financial close: Check final numbers against the financial close checklist for accuracy (e.g., Blackline).

Next Steps: Review back-office tasks to rank them by time spent. Outline how the problem is currently solved, and then trial using large LLMs to solve it.  Depending on the results, search for current tools that use AI to solve the challenge you are facing.

2. Navigate KYC, AML, and GDPR Processes with AI

Compliance processes like KYC, AML, and GDPR are resource-intensive, and the risk of fines from non-compliance is high. AI can efficiently analyze documents, extract key data points, and identify risks based on jurisdiction. Automating these processes reduces compliance risk without significantly increasing costs.

• Document verification: Real-time verification of user-submitted documents to meet KYC and AML requirements (e.g., Onfido).

• Policy review automation: Batch-review policies to ensure compliance with upcoming regulations (e.g., ClauseMatch).

• Create new policies: Generate new policy documents using best practices to ensure proper documentation (e.g., Scribe).

• Ensure compliance: Assess your entire setup to ensure full compliance with internal policies (e.g., Vanta).

Next Steps: Identify your largest compliance risks. Centralize contracts and policies. Test AI-driven compliance solutions to streamline processes.

3. Improve Your Team’s Productivity and Understanding

FinTech requires specialized knowledge, making onboarding and upskilling challenging. AI simplifies this by delivering curated resources, automating document reviews, and supporting brainstorming. This allows you to quickly get your teams up to speed, serving as an example to the rest of the organization by:

• Create onboarding plans: Develop comprehensive checklists that your team needs to know (e.g., Gusto).

• Monitor onboarding: Generate quick tests to ensure your key hires retain key knowledge during onboarding (e.g., Workday).

• Improve understanding: Provide automated, simplified summaries to convert  (e.g., Plaid).

• Educate: Use generative AI tools to explore FinTech terms interactively and enhance learning (e.g., ChatGPT).

Next Steps: Review your onboarding process and introduce checkpoints or tests. Use generative AI to create thorough checklists and ensure the onboarding process is comprehensive and efficient.

4. Respond Efficiently to Customer Needs

While not strictly under the CFO control, some of the major wins are in Customer Service where AI delivers immediate value by automating responses and streamlining interactions. AI-powered chatbots efficiently manage large volumes of inquiries, resolving issues faster than human teams. Advanced AI systems can also assist customers with more complex tasks, such as loan applications and troubleshooting. How much time of your support team is wasted re-explaining material already in your knowledge base?

• 24/7 support: Chatbots handle inquiries anytime, improving response times and customer satisfaction (e.g., Chime).

• Guided assistance: Provide customized advice to help customers through complex tasks, like loan applications, reducing human intervention (e.g., SoFi).

• Enhanced experience: AI improves consistency, accuracy, and speed in customer interactions, boosting satisfaction and retention (e.g., Robinhood).

Next Steps: Explore the current solutions with the head of your CS business partner.  Explore the different solutions that exist today.

Automate Flags for Overextended CFOs

5. Supercharge Data Analytics and Projections

Amazon is known for tracking thousands of metrics in management meetings, but most growing FinTech companies don’t have the time to analyze that much data on a weekly basis. Focusing on a few metrics (e.g., revenue, customer usage) while others (like CAC payback, per-customer profitability, non-payment) may slip. Expecting everyone to track every report isn’t practical. Machine learning can predict trends, detect anomalies, and automate financial forecasting across thousands of data points, surfacing the most critical points to your team.

• Summarize disparate data: Create summaries across metrics, pinpointing areas with the biggest impact (e.g., transaction volume, key customers, usage).

• Immediate flags: Run reviews daily to provide real-time alerts for any major risks or anomalies.

• Real-time forecast updates: Use AI to break up your forecasts into smaller intervals and update reforecasts based on how closely you’re tracking to targets.

• Key metric reviews: Generate text and visual reports to accompany “just the numbers” for management, highlighting concerns in a way everyone on the team understands.

Abacum is particularly well-positioned to deliver on all of these through its ability to consolidate data and provide live dashboards with AI assistance.

Next Steps: Centralize your financial data and key metrics. Create a checklist of critical metrics and acceptable ranges and use AI to monitor and flag deviations for proactive management.

6. Truly Understand the “Why” Behind Your Key Metrics

Priorities shift, and markets change. It’s not enough to know what’s going wrong—you need to understand why to make informed decisions. With the right setup, AI models can drill down into key metrics like CAC, LTV, and churn to provide actionable insights. This helps optimize marketing spend, improve retention, and drive better ROI. For example, Revolut uses AI to reduce CAC by 20% compared to traditional banks.

• CAC tracking: Monitor and adjust marketing spend in real-time, improving cost efficiency based on funnel performance.

• LTV analysis: Link customer behavior to retention and upsell opportunities, giving a clearer picture of lifetime value and how to increase it.

• Churn reduction: Identify early churn risks through usage, communication, and payments, enabling proactive retention strategies.

Next Steps: Ensure your tools can integrate across systems to provide a complete view of your key metrics for better decision-making.

Respond quickly and transparently

7. Manage Volatile Revenue Streams and Pricing

Revenue models in FinTech, particularly those dependent on transaction fees, interest rates, or payment volumes, can be highly volatile. AI can help stabilize these revenue streams by predicting trends, analyzing user behavior, and optimizing pricing models to adjust to macroeconomic factors.

• Dynamic pricing: Adjust promotions and fee structures based on user behavior, optimizing revenue and conversions (e.g., Monzo).

• Interest rate adjustments: Optimize loan interest rates, adjusting based on market conditions and individual borrower risk profiles (e.g., SoFi).

• Transaction fee optimization: Adjust transaction fees for merchants, helping them balance pricing and competitiveness in real-time (e.g., Stripe).

• Subscription pricing: Evaluate regional market conditions and adjust subscription pricing dynamically to maximize retention and revenue growth (e.g., Netflix).

Next Steps: Firms should consider AI-driven predictive models to optimize pricing strategies and stabilize revenue in fluctuating market conditions and be the first to respond to macroeconomic changes.

8. Accelerate Fundraising Cycles with AI

Fundraising is crucial for many FinTechs, and AI can greatly streamline this process by generating real-time financial dashboards for potential investors. AI enhances transparency, builds trust, and shortens funding cycles. For example, Nubank used AI to secure $750 million by offering investors detailed, real-time financial insights.

• Real-time investor reporting: Provide AI-powered dashboards to investors, delivering live financial performance insights to provide confidence (e.g., Nubank).

• Faster due diligence: Use AI to automate the collection and analysis of financial data, significantly accelerating the due diligence process during fundraising rounds (e.g., Revolut).

Next Steps: AI cuts down the time to provide this reporting. By leveraging the reduced amount of time, you can keep your investors deeply informed, enabling them to provide more value and assist in future fundraising.

In Conclusion

Adopting AI allows you to scale quickly and efficiently with fewer resources. While there’s a lot to consider, the key to unlocking AI’s full potential lies in having centralized data sources and a company culture that embraces innovation.

Building this infrastructure yourself can be time-consuming. That’s why we created Abacum—to centralize reporting across all your tools, integrate projections into your workflow, and offer a suite of AI-powered features, including automated notifications, summaries, and scenario analysis. This empowers your team to take advantage of the increased effectiveness AI can deliver to companies today.

Appendix: Future Trends – AI in FinTech FP&A

In the next 6-12 months, we will see major advancements in AI tools for FinTech FP&A. These improvements will reshape how companies manage financial operations, offering even greater efficiency and strategic insight.

• AI agents: AI agents will become more autonomous, proactively monitoring metrics, identifying risks, and offering solutions without human intervention. They will act as virtual assistants, managing everything from cash flow to compliance.

• Advanced scenario planning: AI will enhance scenario modeling by simulating multiple market conditions in real time, helping companies make better-informed decisions and manage risk more effectively.

• End-to-end AI ERP: ERPs will continue to develop, enabling automated accounting and reporting from documents, sales transactions, and integrations. Accountants will be better positioned to review data rather than performing data entry or reconciliations.

The best way to future-proof your business is by using a tool like Abacum, ensuring you stay up-to-date with cutting-edge FP&A technology that continuously evolves with AI advancements.