What is AI in finance?
AI in finance refers to the application of intelligent technologies, such as machine learning (ML), deep learning (DL), and natural language processing (NLP), to improve financial operations, decision-making, and service delivery.
These systems process large volumes of data to detect patterns, predict trends, and streamline workflows. AI empowers finance teams to work faster and smarter, enabling informed decision-making, greater accuracy, and responsiveness in a dynamic economic environment.
In this article, we discuss:
What are the key benefits of artificial intelligence in finance?
What are the challenges surrounding AI in finance?
How are machine learning and deep learning used in finance?
What are some common use cases for AI in finance software?
What does the future of AI in finance look like?
Benefits of AI in finance
AI delivers tangible business outcomes across finance functions. Leading benefits include:
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Operational efficiency: Repetitive tasks like payroll runs, invoice matching, and account reconciliation can be automated, freeing finance teams to focus on value creation.
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Cost reduction: By reducing reliance on manual tasks, AI drives down operational costs while increasing data quality and consistency.
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Customer personalization: NLP-powered chatbots and virtual assistants enhance user experience by delivering tailored support, 24/7.
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Faster decision-making: Real-time analytics and predictive models inform decisions by identifying financial risks, market trends, and business performance indicators.
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Fraud mitigation: AI monitors transaction patterns and detects anomalies, improving accuracy and reducing time-to-resolution for suspicious activities.
According to IDC, 26% of CFOs say faster decision-making is a top priority for AI investment, while 24% prioritize improved compliance and risk controls. Yet, many AI initiatives fail due to a lack of clear focus on use cases or internal skill alignment.
Challenges surrounding AI in finance
While the upside is significant, successful AI adoption requires overcoming several structural and technical challenges:
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Data governance: Financial data is sensitive. Maintaining compliance with evolving data privacy and security regulations is critical.
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Bias and accuracy: AI models are only as reliable as the data they are trained on. Poor data quality or biased datasets can undermine trust.
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Regulatory complexity: Navigating region-specific requirements (such as GDPR or SOX) demands adaptable, auditable AI systems.
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Workforce readiness: Upskilling finance professionals to collaborate with AI systems remains an enterprise-wide imperative.
A Unit4 global study found that 83% of finance professionals expect to upskill in AI within two years, with adoption strongly correlated to higher confidence in leadership and business performance.
Applications of machine learning and deep learning in finance
Machine learning
Machine learning (ML), a subset of AI, is widely used in finance to analyze datasets, predict market trends, and optimize investment strategies. Financial institutions leverage ML to identify patterns and make data-driven decisions in real time. Machine Learning and Deep Learning are core to today’s financial innovation.
Deep learning
Deep learning (DL) is a specialized subset of Machine Learning that uses artificial neural networks with multiple layers (hence "deep") to model and solve complex financial problems, analyze large datasets, and make predictions or decisions. Its ability to process vast amounts of structured and unstructured data makes it a powerful tool for the financial industry.
To summarize briefly:
Machine Learning: Focuses on algorithms to learn from data.
Deep Learning: A more advanced form of ML using neural networks to handle complex tasks.
Here’s how ML and DL are driving value:
Risk assessment
AI models evaluate credit scores, cash flow trends, and payment history to support real-time risk profiling. This reduces default rates and enables more informed lending decisions.
Fraud detection
ML systems continuously monitor transactions, learning from historical behavior to instantly flag abnormal activity and reduce fraud exposure.
Algorithmic trading
DL models process millions of data points to forecast asset performance, giving hedge funds and institutions an analytical edge in trade execution.
Customer service
Virtual assistants powered by NLP automate client interactions, delivering balance updates, payment tracking, or loan explanations without human intervention.
Financial product personalization
AI-tailored offers based on user preferences, behaviors, and risk appetites improve conversion rates and long-term loyalty.
According to Dresner Advisory Services, over 50% of enterprise performance management (EPM) users report that predictive forecasting and automation in finance have the highest impact, particularly in budgeting and scenario planning.
AI-powered financial software examples
AI is deeply embedded in Unit4’s financial software, enabling human-centric, automation-first design across FP&A, ERP, and HCM platforms. Here are some examples of artificial intelligence use within software modules:
Generative AI for reporting
AI-driven storytelling in Unit4 FP&A transforms raw numbers into business narratives, identifies deviations, and recommends next actions.
Invoice processing
Smart AI assistants cut invoice cycle times by 30% and boost multi-client processing efficiency by 90%.
Payroll automation
Unit4’s Payroll Navigator reduces manual tasks by 30%, cuts errors by 50%, and speeds onboarding by 40%.
Bank statement reconciliation
Pattern recognition accelerates matching and reduces time spent on exceptions.
A UK public healthcare provider using Unit4 FP&A reduced monthly reporting time by 90%, shortened cost center forecasting from 2 hours to 30 minutes, and consolidated budgets across hundreds of teams, supporting real-time decisions for a population of over 6 million.
The future of AI in finance
AI is a foundational capability for future-ready finance teams. What’s next:
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Predictive insights at scale: Forecasting behaviors and trends will become embedded in all financial processes, not limited to planning cycles.
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Embedded intelligence: AI will move beyond core finance into procurement, workforce planning, and capital allocation.
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Hyper-personalization: Financial services will be customized by default, driven by continuous data feedback loops.
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AI-augmented finance teams: Automation will handle compliance and reconciliation, allowing teams to shift focus to scenario modeling and business partnering.
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Proactive compliance: Built-in auditability and explainability will ensure AI systems align with evolving regulations, including sustainability reporting standards.
The future of finance lies in becoming the storytellers of business performance… Yes, roles will evolve, but finance professionals will be critical to the successful adoption of AI.
Michael Lengenfelder
VP FP&A Product Management, Unit4
Empower your financial operations
The competitive finance function of the future is intelligent, agile, and people-first. AI isn’t about replacing teams—it’s about empowering them to deliver faster, more strategic, and more personalized outcomes.
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