AI is transforming how B2B finance operates, streamlining payments, powering credit decisions, driving more accurate forecasting, and improving fraud detection. Across financial institutions and corporate finance teams, it is becoming an essential tool for efficiency, risk management, and strategic growth.
While adoption is accelerating, the shift to AI-driven finance brings new challenges around data integration, transparency, and oversight. Understanding where the industry stands today is key to navigating this change.
This blog explores the key statistics, innovations, and use cases shaping AI in finance and how these technologies are redefining the future of financial operations.
The Rise of AI in Finance: Market Growth and Adoption Trends
AI is now a core component of the tool stack of finance teams, and its influence is expanding rapidly. Growing investment, wider adoption, and measurable economic impact show how AI is impacting financial operations and setting new standards for efficiency and performance across the industry.
- Economic contribution. AI is driving massive growth in finance, with PwC projecting a $15.7 trillion contribution to the global economy by 2025. Finance teams are turning to AI to streamline operations, improve risk assessment, and enhance forecasting accuracy.
- Growing investments. According to Statista, AI spending in finance is on a steep rise, with investment set to grow from $35 billion in 2023 to $126.4 billion by 2028, showing a strong commitment to AI-driven solutions in the industry.
- Adoption surge. More financial institutions are embracing AI, with 58% of organizations already using it for fraud detection, predictive analytics, and financial reporting—up from 37% in 2023, according to Gartner.
- Bigger budgets. Financial institutions are significantly increasing their AI budgets. According to IDC, banks and financial service providers are expected to spend $97 billion by 2027, more than double their current spending, focusing on AI for fraud detection, compliance, and customer service improvements.
AI in Risk Management and Credit Decisioning
Now becoming a cornerstone of modern risk management, AI is helping finance teams make faster, more accurate credit decisions while maintaining control over exposure. According to McKinsey, 60% of financial institutions are actively using or exploring AI for portfolio monitoring, while just over 40% are focusing on AI-driven credit applications, risk controls, and regulatory reporting. Finance teams see slightly more potential for AI in wholesale credit risk than in retail credit.
1. Credit application
AI is transforming how credit applications are processed by automating data collection, verification, and analysis. Instead of relying on days of manual review, AI can assess a company’s financial health in seconds, drawing from sources such as bank transactions, invoices, and behavioral data. This automation reduces friction for applicants and gives lenders a broader, more accurate understanding of creditworthiness.
2. AI underwriting & decisioning
AI underwriting combines multiple data streams with advanced modeling to deliver real-time, predictive credit assessments. It performs two critical functions: first, structuring large volumes of raw financial data for analysis, and second, enabling automated, data-driven decision-making that can assist analysts or operate independently.
Research shows that AI models enhance credit risk evaluation by improving predictive accuracy, automating decision-making, and incorporating alternative data. These advancements lead to higher loan approval rates and lower default risks. One study found that AI models analyzing non-financial data can expand credit access by 40% for individuals without a credit history.
Modern AI systems also make adaptive decisions in real time, adjusting exposure levels as new information becomes available. For lenders and merchants, this means underwriting that scales efficiently while maintaining control and less risk.
3. Credit Monitoring
Unlike static reviews, AI systems continuously learn from repayment and utilization data to detect early signals of financial stress. Credit limits can rise automatically for strong-performing buyers, while accounts showing distress are flagged for closer monitoring. This creates self-adjusting portfolios that are healthier, more resilient, and better aligned with real operating conditions. Continuous monitoring also supports compliance by providing a real-time view of credit exposure and enabling faster responses to emerging risks.
AI is enabling real-time risk monitoring, improving underwriting accuracy, and expanding financial inclusion. Traditional credit models rely heavily on limited bureau data and financial history, leaving many potential borrowers without access to credit. AI addresses this gap by incorporating alternative data, such as transactional behavior and digital activity, to provide a more complete risk profile. This allows lenders to approve more loans while maintaining lower default rates. Beyond credit access, AI-driven models adapt to market shifts, enable more accurate financing decisions, and detect early risk signals.
AI in Fraud Detection and Security
As digital transactions grow in volume and complexity, financial institutions face increasingly sophisticated fraud schemes. AI enhances protection by analyzing patterns, detecting anomalies in real time, and preventing fraudulent activity before it impacts customers.
1. Fraud loss reduction
AI-powered fraud detection systems have significantly lowered fraud losses. For example, J.P. Morgan reported a 15–20% reduction in account validation rejection rates after implementing AI-driven payment validation screening.
2. Real-time detection
AI enables continuous monitoring of transaction activity to identify fraudulent anomalies instantly. The Commonwealth Bank of Australia (CBA) reported a 50% reduction in scam losses after integrating AI-driven fraud prevention technology. Additionally, AI-powered suspicious transaction alerts contributed to a 30% drop in customer-reported fraud cases.
3. Better fraud detection accuracy
AI-driven fraud prevention is improving transaction security across financial institutions. Mastercard, leveraging AI, has achieved a 20% increase in fraud detection rates by analyzing transaction patterns and external fraud signals in real time. This helps reduce fraud losses while minimizing false positives.
Traditional fraud detection systems struggle to keep up with evolving fraud tactics. AI learns from new transaction patterns, making it faster and more effective at identifying threats. In addition, real-time AI monitoring stops fraudulent transactions before they happen, reducing financial losses. It also minimizes false positives, so legitimate payments aren’t blocked. As AI adoption grows, finance teams will enhance fraud prevention, improve compliance, and build stronger customer trust.
AI in B2B Payments and Accounts Receivable Automation
AI is reimagining how businesses manage payments and receivables. By automating the process from invoicing to collections, and forecasting, it helps finance teams become more efficient, speeding up cash flow, reducing errors and operational complexity, and improving working capital.
1. Faster payment processing
Accounts receivable automation helps businesses accelerate cash application, minimize manual work, and improve payment efficiency. According to PYMNTS, firms with automated payment acceptance have an average DSO of 40 days, compared to 47 days for those without automation. Additionally, 87% of businesses with automated AR report a significant boost in overall process speed.
2. Enhanced cash flow management
AI-powered forecasting helps businesses anticipate shortfalls, optimize working capital, and improve accuracy. A Deloitte survey found Gen AI’s biggest impact will be on cash flow forecasting, with over 60% of finance leaders recognizing its potential. As financial complexity grows, AI will be key to stabilizing cash flow and managing risk.
3. Smarter cash application and collections
AI and automation are streamlining the entire post-payment process, from matching payments to invoices to prioritizing overdue accounts and automating outreach. According to PYMNTS and American Express, companies that automate their accounts receivable operations can reduce collection times by up to 67%, accelerating cash flow and minimizing manual workload. By combining predictive analytics with automated reconciliation and follow-ups, finance teams can post cash faster, lower delinquency rates, and gain clearer visibility into working capital.
Recognizing the benefits, PYMNTS revealed that 78% of middle-market CFOs plan to increase their AI budgets in 2025. This reflects a growing commitment to leveraging AI for accounts receivable processes.
AI in finance is eliminating bottlenecks in B2B payments and accounts receivable, helping businesses get paid faster, reduce risk, and improve cash flow. By automating invoicing, collections, and forecasting, companies can streamline operations and make smarter financial decisions. As more CFOs invest in AI, companies can better predict late payments, automate follow-ups, and optimize working capital, reducing bad debt risk and improving liquidity.
AI in Financial Planning and Forecasting
Financial planning is becoming increasingly data-driven, and AI is at the center of that shift. It helps finance teams forecast more accurately, streamline reporting, and plan with greater confidence amid constant market change.
1. Strategic planning
As AI adoption accelerates, CFOs are prioritizing analytics and reporting to enhance financial decision-making. A Gartner survey of 251 CFOs found that AI adoption in finance is now the fourth-highest priority for 2025, reflecting a shift toward leveraging AI for more accurate planning and forecasting.
2. Reporting and auditing
According to KPMG, nearly 75% of businesses are already using AI in financial reporting, and adoption is projected to reach 99% within three years, confirming its importance in streamlining financial processes and ensuring accuracy.
The future of financial planning belongs to businesses that embrace AI. It enhances forecasting accuracy, automates data analysis, and enables proactive decision-making. CFOs are increasingly relying on AI-driven insights to improve budgeting, streamline financial reporting, and optimize resource allocation. Moreover, beyond forecasting, AI’s role in financial reporting and auditing is expanding rapidly, improving transparency, reducing errors, and strengthening compliance.
How Balance is Powering the Future of B2B Commerce and AI in Finance
Balance is redefining B2B commerce with an agentic financial infrastructure that replaces fragmented, manual workflows with a single platform built for scale. Instead of adding AR overhead, and building complex payment operations, merchants use Balance to offer flexible payment terms, streamline their invoice-to-cash operations.
Below are examples of how Balance applies AI across the invoice to cash lifecycle to eliminate manual tasks, reduce DSO, and accelerate cash conversion:
AI-Powered Underwriting
Balance’s credit risk engine evaluates a broader and deeper data footprint than traditional bureaus, enabling accurate real-time decisions Merchants see up to 4× higher approval rates, while continuous monitoring allows for automated dynamic limits adjustment, giving buyers more purchasing power without added risk.
Agentic AR Tools
Collections Co-Pilot
Balance’s collections module includes an AI agent that prioritizes cases, recommends next steps, drafts outreach, and gives teams the context they need to resolve issues quickly.
Context-Aware Cash Application
Balance’s Cash Application module automates invoice matching with 95% accuracy. By analyzing real time order, delivery, billing, and dispute data the AI agent is able to identify short-pays, link deductions to their root causes, and help AR teams clear exceptions faster. It transforms cash application from a manual matching exercise into an AI-driven workflow that explains discrepancies and recommends resolutions instantly.
Real-Time Intelligence Through Balance’s MCP Server
By connecting LLMs to Balance’s MCP server, merchants can easily query Balance’s credit, payments and AR APIs to ask questions, surface insights, and take action directly from their preferred AI interface.
These capabilities create a streamlined B2B payment experience for business buyers and can be scaled across all customer segments without adding operational overhead for merchants.
The Road Ahead for AI in Finance
AI is reshaping the finance function, streamlining payments, strengthening credit evaluation, modernizing AR, and enhancing financial planning. Teams that lean in early are operating faster, reducing uncertainty, and unlocking insights that translate directly into business growth.
