AI in Financial Services: How to Apply AI Across Regulated Financial Workflows

AI is seen as the savior of everything from retail banking to capital marketing. But, looking at the day-to-day operational reality, many firms are stuck in the earliest stages of automation. They’ve run dozens of pilots, many of which show promise in a vacuum, but which then break down the moment they hit the messy, regulated, and fragmented world of real financial services workflows.

Business leaders want more: more speed, more efficiency, more savings, more automation. And, they want it now. Meanwhile, regulators are watching every move, leaving risk and compliance teams hesitant to let a non-deterministic technology loose on mission-critical processes. Sure, AI is already making headway in fraud detection, credit scoring, onboarding, customer operations, and compliance monitoring, but automation deployments still stall due to fragmented systems, manual handoffs, and weak governance.

What leading financial services firms are really looking for with AI is better decision-making, less risk, and faster, more accurate processes. To get there, it’s time to look beyond the hype and focus on how AI can be embedded into end-to-end workflows that involve existing systems, human oversight, and strict compliance obligations.

This article provides a strategic framework for integrating AI into financial services processes without losing sight of security, fairness, explainability, accuracy, control, and auditability — just to name a few. It also explores high-value AI use cases in financial services, related operational challenges, how to embed AI into core workflows, and how agentic process automation (APA) provides a safe platform for scaling AI.

What is AI in financial services?

AI as a concept encompasses many technologies. In general, AI solves problems, executes tasks, makes decisions, improves as it learns, and more. Traditional AI uses pattern recognition to act through probabilistic predictions. More recently, generative AI added the ability to understand, interpret, and create new content quickly and at scale. Today, AI has advanced to include contextual reasoning, where data is not only captured or created, the data’s context and intent are understood to inform or guide subsequent actions.

AI in financial services is defined as the strategic application of AI-driven automation to support decision-making, detect patterns, mitigate risks, and optimize end-to-end workflows across banking, insurance, and capital markets.

Rather than functioning as a standalone assistive tool, AI serves as an active participant in regulated workflows such as onboarding, underwriting, lending, know your customer (KYC) and anti-money laundering (AML) protocols, and claims processing. Its operational roles are diverse and complementary to core processes, and can be applied to:

• Risk and Credit Scoring: Analyzing vast datasets to determine probability of default or creditworthiness.
• Intelligent Document Automation: Extracting and validating structured data from unstructured sources, such as handwritten invoices or complex legal agreements.
• Anomaly and Fraud Detection: Identifying suspicious patterns in transactions that deviate from established norms.
• Workflow Acceleration: Guiding processes by identifying the "next best action" based on real-time enterprise context.

In a professional financial services environment, these AI automations require a specific combination of predictive models, natural language processing, and LLM-powered document analysis, and fraud-signaling algorithms. All the while, these solutions must also prioritize explainability and fairness to satisfy customers and regulators.

Why AI matters in financial operations today

The shift toward AI-driven operations is driven by a perfect storm of industry pressures. Financial institutions are currently managing rising operating costs, margin compression from new competitors, the complexities of interest rate and market dynamics, and ever-more-innovative fraudsters. Simultaneously, the regulatory landscape has become increasingly volatile, particularly in requiring meticulous documentation for AML/KYC compliance. This confluence of factors makes AI not just a nice-to-have; AI is a strategic imperative for financial services firms.

AI addresses these challenges by acting as a force multiplier for manual review processes. By automating the data-heavy aspects of document interpretation and multi-system data gathering, AI reduces operational bottlenecks and ensures consistent execution across global teams. This allows financial services firms to meet rising customer expectations for personalized experiences with consistent execution across segments and regions without compromising their risk posture. And, it drives faster cycle times for everything from loan approvals to claims processing.

Ultimately, firms need improved decision quality without compromising fairness or auditability. AI excels at surfacing patterns and insights that humans might miss, offering a powerful advantage. However, this intelligence must be paired with strong governance and human oversight.

Ultimately, the real value of AI emerges when it is not simply used for isolated tasks, but seamlessly embedded across full workflows to set the stage for truly impactful operational use cases.

High-value AI use cases in financial services

The most successful AI applications aren't found in isolated tasks; they're found in workflows that rely on repetitive review, document analysis, and multi-step decisions. Let's explore the most impactful use cases for AI in financial services.

AI for broker-dealers

Financial professionals working as broker-dealers are under constant pressure to serve more clients with more products while maintaining compliance with complex regulations. AI helps broker-dealers with matching client profiles with investment products, highlighting relevant regulatory requirements, and detecting potentially illicit activity. AI also takes on back-office tasks such as customer support and onboarding, giving broker-dealers more time to engage with valuable clients.

AI in wealth management

AI helps financial advisors deliver hyper-personalized service by quickly analyzing a client's complete financial picture, from risk tolerance and investment goals to existing assets and behavioral patterns. AI can then suggest and package customized portfolios and plans. AI also assists advisors by summarizing market research, economic reports, and news to identify potential investment opportunities or emerging risk factors, even drafting personalized client communications to bolster client relationships.

AI in insurance

AI assists insurance providers by automating the many manual, document-intensive processes that support assessing, pricing, and managing insured risks. AI also analyzes historical claims data, demographic information, and other data to create more accurate risk profiles for new policies and existing renewals, delivering more precise pricing for higher profitability. To improve customer engagement, AI can suggest personalized product recommendations based on individual customer needs and preferences.

AI in lending and credit decisioning

AI supports the entire lifecycle of a loan, from extracting data from income documents, to verifying identities, to providing a preliminary risk score. The win here isn't just speed; it's the elimination of inconsistencies that occur when different human reviewers look at similar files. AI maintains audit-ready explanations for every approval or denial, consistently.

AI in fraud detection and transaction monitoring

Traditional rules-based systems are great at catching known threats, but they struggle with novel approaches employed by clever criminals. AI identifies and scores suspicious patterns in transaction streams in real-time. When a flag is raised, AI can summarize the reasoning and feed it directly into an investigation workflow for human review of the issue and its full and transparent context.

AI in claims processing and underwriting

In insurance and other financial services, claims are often the biggest operational bottleneck. AI extracts and validates claim data from hundreds of variations of documents, ensuring that nearly all claims enter "straight-through processing" for consistent outcomes and no need for human intervention. For the edge-case claims that are complex or outliers, AI triages the case and routes it to the right human specialist.

AI in KYC, AML, and compliance reviews

KYC and AML are notoriously resource-intensive. AI agents can extract identity data, calculate risk scores, and apply regulatory logic to validate documents. AI also creates a defensible audit trail of every step taken, which augments human oversight and provides lineage essential for compliance testing and regulatory reporting.

AI in customer operations and service requests

AI acts as an agent assistant to classify requests, summarize ticket history, retrieve data from CRM systems, and suggest the next-best action for a customer service representative. This keeps customer cases moving at a fast pace while preserving decision controls and communication guardrails.

Once again, the greatest value emerges when AI supports multiple steps in an end-to-end process, not just isolated tasks stitched between human effort. When AI is woven into the fabric of an entire process, its impact multiplies for more impactful operational improvements.

Challenges financial institutions face when implementing AI

Implementing AI is not without its challenges, especially without proper planning and preparation. While fundamental model concerns like bias, drift, and data quality are important, the real reason AI fails in production is usually related to workflow and governance layers. When organizations lack clear task handoffs, controls, auditability, or visibility across the end-to-end process, even a well-trained model can produce inconsistent or unpredictable outcomes.

Let’s drill down on a few common AI challenges faced by those in the financial services industry:

• Fragmented Systems and Siloed Data: Financial workflows often span several legacy systems that don't talk to each other. AI can't work effectively if the data it needs is trapped in a mainframe, requires a manual export, or resides across systems with inconsistent infrastructures. The result is unreliable AI outputs.
• Explainability and Governance: Regulators want to know the "why" behind a decision. If an AI system is a "black box," it won't pass a compliance review. Many AI initiatives stall when firms can't document how outputs were generated or what inputs were used.
• Manual Handoffs: Many AI pilots focus on a single step (like extracting data), but then task human workers to handle the rest of the process manually. This interrupts the process flow and prevents meaningful operational improvement. Instead, firms should target complete processes, even in the piloting stages.
• Difficulty Scaling: A proof of concept that works on 100 documents often fails when it hits the volume and exception-handling requirements of a real production environment. Plan for scalability beyond pilots, and ensure any AI and automation platforms are capable of supporting the required scale.
• Privacy Constraints: Sensitive data, including personally identifiable information (PII) and payment data, must be handled carefully and is often subject to regulatory requirements. AI initiatives often stall because they lack built-in governance, or the organization hasn't built a mechanism to manage or mask this data before it hits the model or user.
• Cybersecurity and Model Integrity: As financial services institutions adopt generative AI, new risks like prompt injection and data poisoning appear. Luckily, AI can also help to mitigate these risks by masking sensitive, private, or regulated data like personally identifiable information (PII) before it reaches the AI model or an unauthorized user.

There are just a few of the challenges showing why it’s best to approach AI as a solution for complete workflows rather than isolated tasks.

How to embed AI into end-to-end financial workflows

To move past these types of AI implementation and scalability hurdles, think less of AI as a tool and more of it as a part of a governed process. Here is a framework for doing it right:

1. Map the existing workflow: Before deploying AI, get a 100% unbiased map of how the work actually gets done — every handoff, every document, and every system involved. Especially for processes related to lending, claims, AML, and reconciliation, mapping will show which steps require AI, automation, or human judgment
2. Identify where AI adds value (without compromising controls): Not every step needs AI. Use it for classification, verification, risk scoring, or summarization. Keep the subjective, high-risk, or final approval steps in human hands.
3. Pair AI with automation for execution: An AI insight should trigger a subsequent action. Use automation to execute the repeatable steps — like updating a database or sending an email — while AI handles the interpretation.
4. Establish clear rules for escalation: Only predictable logic can determine when an AI agent should handle a task and when it should flag a human for review. Define these rules early to ease later automations or signal when human intervention is required.
5. Maintain full traceability: Every decision, decision path, and decision log must be recorded. Tracking version history, routing paths, and decision context creates the lineage required for auditability and potential regulatory scrutiny.

Keeping this framework in mind prepares financial institutions for agentic automation supported by APA.

Bringing AI, automation, and human judgment together with APA

AI alone cannot automate a full workflow. Real success requires APA, where AI agents, RPA, APIs, and human workers are coordinated from a single platform.

APA acts as an orchestrator that unifies all the parts of the automated workflow. It uses "process agents" that can operate across underwriting systems, CRM platforms, and compliance databases. At the heart of this system is a reasoning engine that brings context to the automation. AI relies on reasoning to identify the next best step — like whether to route a claim for payment or escalate a transaction for suspicious activity — based on real-time data and enterprise intelligence. And, using retrieval-augmented generation (RAG), APA accesses real-time enterprise data to inform actions, increase output accuracy, and understand business and regulatory contexts.

An advantage of an APA platform is that it includes built-in governance guardrails. These controls automatically mask PII, block toxic language, and enforce AI and data usage policies before AI executes an action. This helps firms scale across different domains — lending, fraud, AML — without increasing regulatory or compliance risk.

How Automation Anywhere supports AI in financial services

Automation Anywhere provides the foundational infrastructure financial services firms need to transition from pilots to production-scale AI automations. Our platform is built for the accuracy and auditability required in financial services workflows.

The core value of the Agentic Process Automation System is its ability to let AI agents, RPA, APIs, and humans work together in one governed workspace. The brains behind APA is the Process Reasoning Engine (PRE), which provides the context-aware intelligence needed to align with regulatory and security expectations, while AI Agent Studio enables teams to build custom agents that follow specific business rules.

Automation Anywhere’s platform catalyzes AI scalability and success in the financial services industry:

• Alight, which provides wealth and health systems, uses Automation Anywhere to automate high-volume claims approval processes. By integrating AI, it achieved 95% accuracy, cut processing times from days to hours, and reduced call volumes by 50%.
• Osaic, a leading broker-dealer, uses intelligent automation to streamline advisor-focused workflows. It automated dozens of processes to achieve a 186% ROI in the first year, cutting 25,000 from manual work annually and closing 66% of cases at least one day earlier.

These results aren't just about speed; they're about building and scaling financial services AI automations that maintain auditability, security, and governance at every level — all without disrupting existing systems or processes.

Learn how AI can automate financial services processes today

Ready to transition AI initiatives from pilots to production-scale reality? Get a personalized demo to see how the Agentic Process Automation System can securely orchestrate complex financial workflows while maintaining strict compliance.

Request a live demo today.

FAQs

How can financial institutions evaluate whether a workflow is appropriate for AI involvement?

Ideal candidates for process automation are high-volume, document-centric workflows using structured or unstructured data, such as KYC document intake or loan processing. Use process discovery tools to identify areas where manual handoffs create bottlenecks and where AI-driven classification or extraction can yield a clear ROI.

What types of controls ensure AI outputs meet regulatory expectations?

Controls include built-in governance controls and guardrails that perform real-time safety checks, such as masking PII or payment information. Use platforms that shield processes from operational risk and fraud by strengthening security, compliance, and controls.

How should institutions balance AI automation with human oversight in sensitive decisions?

Adopt a collaborative approach where AI handles data gathering, summarization, and risk flagging, while human workers retain final decision-making authority. Keeping a human-in-the-loop (HITL) applies AI to routine heavy lifting while human expertise focuses on complex exceptions or high-value judgments.

What does it take to scale an AI proof of concept into a production workflow?

Scaling AI automations requires moving from isolated AI tools and pilots to a unified platform that provides centralized governance, observability, and infrastructure. Organizations should invest in upskilling workers to ensure that business users actively participate in discovering and prioritizing automation candidates and managing automated workflows.

How can legacy systems be integrated without major modernization?

Agentic automation uses RPA, APIs, and human collaboration to bridge the gap between old and new technologies. RPA can interact with legacy systems that lack modern connections, while the APA orchestration layer coordinates data exchange between these systems and AI agents via secure APIs.

What metrics should institutions track to measure AI’s operational impact?

Key metrics for financial services AI automation evaluation include straight-through processing rates, reduction in cycle times, and error reduction rates. Firms can track ROI through hours of capacity saved, improvements in customer satisfaction scores, and more.