By Giannine Cohoon, Senior Vice President, Robert Half Managed Business Solutions
AI continues to reshape financial services, driving smarter and faster operations. From chatbots handling customer inquiries to machine learning algorithms detecting fraud in real-time, financial institutions are deepening their reliance on AI to enhance efficiency and security.
If your company is exploring the use of AI in financial services, here’s a look at some notable AI applications. Later, we’ll discuss the all-important need to start assessing whether your existing team has the expertise to maximize value from AI.
1. Automating data entry and financial reporting
One of the most impactful ways AI is transforming the finance industry is by reducing repetitive, time-consuming data entry tasks. With advancements in machine learning, AI can process transactions and account statements in seconds, freeing professionals to focus on more strategic work like financial analysis and decision-making.
But the real value of AI goes far beyond automation. In financial reporting—one of the most closely scrutinized areas—AI is helping professionals analyze large and complex datasets to uncover trends and perform predictive analytics. This allows teams to better understand the current state of the business and make more informed decisions about the future.
Research shows, 65% of financial leaders use AI in their reporting processes. These tools not only extract real-time financial data but also incorporate the latest regulatory standards, reducing human error and strengthening compliance.
Success in this landscape requires a blend of hard and soft skills. Tools like Power BI and Microsoft Copilot can help analyze massive datasets, but professionals also need strong financial reporting expertise and emotional intelligence to communicate insights effectively–whether in written reports or executive presentations.
2. AI-powered chatbots for personalized customer support
Customer support in the finance industry faces a constant influx of calls, messages and chats, with customers expecting fast, efficient service. AI in financial services is transforming this landscape by enabling 24/7 assistance through conversational AI and Natural Language Processing (NLP). Banks and financial firms now use AI-powered chatbots to provide personalized, real-time responses, improving customer satisfaction and operational efficiency.
These chatbots serve as the first line of support, handling routine inquiries while seamlessly escalating complex issues to human agents. However, key performance indicators (KPIs) such as average handle time, call abandonment rate and chatbot opt-out rate remain critical in evaluating the effectiveness of contact centers. A high opt-out rate, for instance, signals a need for better chatbot design or more intuitive escalation paths to live agents.
AI’s role doesn’t stop at automation. When a customer reaches a live agent, AI tools can assist by summarizing account holdings, analyzing customer profiles and even identifying gaps in KYC (Know Your Customer) data. This empowers agents to engage with empathy and active listening, uncovering the customer’s true concerns and resolving issues more effectively.
Agentic AI platforms are rapidly transforming how financial institutions deliver personalized service while safeguarding customer trust. These systems autonomously analyze a customer’s financial footprint and proactively recommend tailored solutions—whether it’s transitioning a client to private banking, offering competitive mortgage options, or introducing wealth management services. By operating with transparency and contextual intelligence, agentic AI not only enhances the customer experience but also reinforces trust, driving long-term retention and sustainable growth.
As more institutions integrate AI assistants into their apps and websites, the focus is shifting from reactive support to proactive relationship building, where every interaction is an opportunity to and deliver value.
3. AI-driven fraud detection and prevention
Fraud detection has evolved far beyond traditional rules-based monitoring. Today, AI plays a pivotal role in identifying and preventing financial crime. In financial services, machine learning and predictive analytics enable AI to analyze massive datasets in real time, spotting suspicious transactions and anomalies before they escalate into serious threats. This proactive approach not only reduces fraud risk but also protects profit margins.
One of AI’s most powerful abilities is continuously learning and adapting. By analyzing transaction patterns, AI can detect emerging fraud tactics and respond faster than legacy systems. Companies rely on machine learning to monitor millions of transactions daily, flagging unusual spending behaviors and stopping fraudulent activity before it occurs.
Professionals in this space are increasingly focused on building models—not just processes—that can identify fraudulent transactions and ensure compliance with regulatory standards. These models are especially critical in areas like alerts investigation and anti-money laundering (AML), where AI helps determine whether an alert falls within the normal range of a customer’s behavior or signals a potential threat.
For example, AI can autonomously detect when a card is used in two distant locations within a short time frame—say, Indianapolis in the morning and Dallas in the afternoon—and prompt a verification alert. If the activity aligns with the user’s travel patterns, it may be cleared automatically. But if the transaction suddenly appears in a high-risk location, the system can escalate it for investigation with minimal human intervention.
This intelligent automation accelerates a bank’s ability to detect and investigate potential fraud or illegal activity, while maintaining alignment with constantly evolving regulatory expectations. The ability to keep pace with compliance requirements—though rarely ahead of them—is a key benefit of AI in this domain.
Technical expertise in AI modeling and fraud typologies is essential, but so is a strong ethical foundation. The human element—especially ethical judgment—is critical to ensuring AI is used responsibly and compliantly. Roles such as compliance analyst, investigation analyst, and business analyst are central to this work, requiring a deep understanding of the full lifecycle of AML and the diverse avenues of fraud.
4. Automated risk assessment for faster decision-making
AI is revolutionizing risk assessment by making it faster, more accurate and more adaptive. Traditionally a slow and complex process, risk evaluation now benefits from AI’s ability to analyze vast datasets in real time—whether assessing market volatility, investment exposure or creditworthiness. Financial institutions are increasingly using AI to go beyond credit scores, incorporating factors like employment history, transaction behavior and business performance to make quicker, fairer lending decisions.
Within banks, risk is assessed across three lines of defense: front office, middle office and internal audit. AI enhances each layer by automating control testing, monitoring user access and flagging anomalies—such as unauthorized data exports or unusual software usage. These tools help institutions identify potential breaches or compliance issues early, ensuring that sensitive data stays protected and internal protocols are followed.
AI also enables faster decision-making by streamlining workflows without compromising regulatory compliance. For example, automation can accelerate mortgage approvals or AML checks, provided every step still aligns with laws like the SAFE Act and the Bank Secrecy Act. Ultimately, effective risk assessment requires a blend of technical expertise, audit skills and ethical judgment to ensure AI is used responsibly and decisions remain sound.
5. Personalized financial services and investment recommendations
AI is redefining personalization in financial services, enabling banks and fintech firms to offer tailored recommendations based on individual customer profiles. From spending habits to income levels and financial goals, AI systems can analyze user data to suggest relevant products—such as mortgage rates or savings plans—through interactive platforms. These tools often begin with a few quick questions, then refine their recommendations as more information is provided, creating a dynamic and responsive user experience.
Robo-advisors are a prime example of this shift. These AI-powered tools assess financial goals, risk tolerance, and behavioral patterns to automate portfolio management. They monitor market conditions in real time and adjust investments accordingly, offering clients personalized strategies without the high costs traditionally associated with wealth management. This not only improves efficiency but also expands access to financial planning for individuals with smaller portfolios.
AI also plays a role in front-office decision-making by profiling users to determine eligibility, recommend products, and even trigger enhanced due diligence when needed. While the process may begin with automated suggestions, it often leads to human interaction—bridging the gap between digital convenience and personalized service. Ultimately, AI empowers financial institutions to deliver smarter, faster and more inclusive financial guidance.
Challenges of using AI in finance
While AI offers transformative benefits to financial services, it also introduces significant challenges—chief among them being data privacy and cybersecurity. As AI systems process vast amounts of sensitive financial and personal data, they become prime targets for cyber threats. Institutions must strike a balance between leveraging AI’s capabilities and protecting customer information, as even minor breaches can erode trust and cause major financial harm.
Regulatory compliance is another ongoing hurdle. Financial institutions must ensure that AI-driven decisions—whether in lending, fraud detection or customer profiling—adhere to constantly evolving regulations. The speed and scale of AI can sometimes outpace compliance frameworks, making strong oversight and ethical governance essential to avoid biased or non-compliant outcomes.
Finally, legacy systems and integration issues pose a major barrier to AI adoption. Many banks operate on outdated infrastructure due to years of mergers and acquisitions, resulting in fragmented systems that don’t communicate well. This “tech debt” limits AI’s effectiveness and scalability. Without modernized systems and a clear integration strategy, financial institutions risk underutilizing AI’s full potential.
Finance and accounting teams face skills deficiencies, notably with regard to AI platforms. According to Robert Half’s Demand for Skilled Talent report, 71% of finance and accounting leaders say they have skills gaps on their team, and 72% of those leaders say the negative impact of those skills gaps has increased in the past year. Partnering with a talent solutions firm like Robert Half can help you quickly find the professionals targeted to your situation, whether you need permanent or contract AI specialists. With the right expertise, your company can harness AI’s full potential while maintaining compliance, security and customer trust.
Follow Giannine Cohoon on LinkedIn.
