AI in financial services has existed longer than you may realize. And now, with the most recent evolutions of artificial intelligence, financial services organizations can integrate AI into their systems to prevent fraud, assess risk, ensure regulatory compliance, and more — faster and more accurately.
Usually, when people think of financial services, their first thought might be a gentleman with a pocket square hunched over a calculator, still working manually to figure out their finances. Or, they think of Wall Street and picture a bunch of stockbrokers crowded around the New York Stock Exchange, waiting for a bell to ring.
Those images are woefully out of date. Most brokers and traders make deals online, and your friend in finance traded in his calculator a long time ago. In fact, artificial intelligence (AI) has been a cornerstone in financial services for over a decade — long before ChatGPT came around — particularly within fraud detection and risk management.
Major banks have used AI techniques like anomaly detection since the early 2010s to automate processes in fraud prevention, cybersecurity and anti-money laundering. In 2023, the financial services industry invested approximately $35 billion in AI, with the banking sector accounting for about $21 billion of this expenditure.
Such an investment, however, requires careful planning — exactly where can you invest this money if AI has already played such a significant role in your organization and industry? Luckily, there are plenty of areas where AI can help advance financial services even further. We’ll explore three such places, and we’ll provide practical guidance on how to integrate these technologies into existing systems.
3 Areas to Expand AI in Financial Services
As mentioned above, AI has already made significant strides in financial services, particularly in fraud detection and risk management. However, its potential to drive further transformation is immense.
AI is now more than just a technological tool — it can be a catalyst for rethinking operational strategies, using data more effectively, and aligning strategic initiatives with organizational objectives. With that in mind, here are three key areas where financial institutions can expand their use of AI.
Customer Personalization at Scale
Forrester’s U.S. Financial Services Customer Trust Index Rankings 2023 found that trust in U.S. financial services firms is relatively low, with only four brands receiving a “strong” trust score. AI can help revolutionize customer engagement by enabling hyper-personalized experiences, including:
- Tailored Product Recommendations: Analyze customer data across ecosystems to suggest the right products at the right time. For example, AI could recommend a home equity loan to customers making frequent home improvement purchases or suggest college savings plans for families with children nearing higher education.
- Proactive Outreach: Use predictive analytics to anticipate customer needs, such as offering support during natural disasters or preemptively addressing billing issues before they become complaints.
- Improved Customer Service: Implement AI-powered chatbots and virtual assistants to handle routine queries, improving response times and allowing human agents to focus on more complex issues. For example, the Commonwealth Bank of Australia has implemented AI systems that have reduced call wait times by 40 percent and even halved losses due to scams.
Expanding AI in customer personalization improves customer satisfaction and increases revenue through better cross-sell and upsell opportunities.
Enhanced Compliance and Risk Management
Financial services is a highly regulated industry due to the amount of sensitive personally identifiable information (PII) it carries. Thanks to this data, it’s often hit with cyberattacks and fraud. And while financial services has its own regulations, it must also comply with the myriad of data privacy regulations based on where its customers live.
Keeping up with all of it can get complicated fast. AI can streamline this process with:
- Proactive Compliance Monitoring: Shift from periodic compliance checks to continuous monitoring by using AI to analyze transactions and public data in real time.
- Efficient AML and KYC Processes: Automate data gathering for anti-money laundering (AML) and know-your-customer (KYC) processes, reducing manual workloads while improving accuracy.
- Fraud Detection: AI can identify unusual patterns across millions of transactions, flagging potential fraud faster than manual systems.
Expanding AI in compliance and risk management ensures regulatory adherence, reduces costs, and minimizes the risk of human error. Citigroup, for instance, recently rolled out AI tools for its employees in eight countries to help them navigate policies and procedures in risk and compliance.
Operational Efficiency in the Back Office
AI can transform back-office operations in financial services by streamlining labor-intensive processes and enabling teams to focus on more strategic work. These improvements not only boost efficiency but also enhance accuracy and scalability:
- Loan Portfolio Management: AI simplifies complex tasks like monitoring loan portfolios for compliance with covenants or analyzing financial statements to ensure regulatory alignment. For example, it can automatically flag inconsistencies or highlight opportunities for portfolio adjustments, reducing the need for manual oversight.
- Document Review and Automation: Back-office teams often handle large volumes of documentation, from quarterly compliance reports to regulatory filings. AI tools can help your organization extract key data, validate information, and compile comprehensive reports faster than traditional methods.
- Cross-System Integration: Many financial institutions rely on multiple platforms for operations. AI-powered integration tools can harmonize data across systems, improving workflows and enabling better insights for decision-making.
High-volume, labor-intensive processes with complex variables are prime opportunities for AI integration in the back office. Upstart, a lending marketplace, uses AI throughout its lending process, including for identity, income, and employment verification. By automating these tasks, Upstart — and other organizations like your own — can optimize resources, reduce operational costs, and create a foundation for long-term scalability and innovation.
AI’s role in financial services is rapidly evolving, with AI now gaining the opportunity to permeate front-, middle- and back-office operations to streamline processes, enhance compliance, and elevate customer experiences. Knowing where to implement AI is half the battle, though — you also need to understand how to get started.
Implementing AI in Financial Services
Integrating AI into financial services may seem complex, but breaking it into manageable steps can make it more approachable. Here’s how to get started with applying AI to increase efficiency, ensure compliance, and improve customer experience:
1. Identify High-Impact Use Cases
Begin by evaluating your organization’s workflows to pinpoint areas where AI can deliver immediate value. Choose one of the three areas listed below to get started.
- Repetitive, labor-intensive processes like document review, compliance monitoring, and loan portfolio management are prime candidates for automation.
- Data-driven decision points, such as customer segmentation or regulatory checks, also offer opportunities for AI to enhance decision-making.
- Customer-facing interactions — such as response times, personalized recommendations, or first-level inquiries — can greatly benefit from AI integration.
2. Start Small with Pilot Projects
Testing AI on a smaller scale helps prove its value and builds momentum before scaling up. For example, you might automate a single aspect of compliance, such as ongoing monitoring of beneficial ownership changes.
Another option is implementing AI for proactive customer engagement, like identifying customers likely to churn or need additional support. A pilot project could also focus on consolidating and analyzing financial statements for commercial loan portfolios. These initial efforts provide measurable outcomes that can guide larger AI initiatives.
3. Use Existing Tools and Expertise
Many financial institutions already use platforms with AI capabilities, such as Salesforce and Dynamics, or they might already have access to AI platforms through their current subscriptions, like Copilot.
Partnering with your software providers to understand their AI functionalities can help integrate these tools into your workflows. Collaboration with consultants or internal experts ensures you can customize AI tools to your specific needs. And, exploring pre-built models for tasks like KYC compliance or customer segmentation can accelerate deployment and reduce initial complexity.
4. Establish Clear Governance and Guardrails
AI implementation requires robust governance to ensure ethical and secure data use. These guardrails should be flexible to allow teams to innovate while protecting sensitive data and adhering to compliance requirements. If they’re too strict, your team members will find their way around them, opening your organization up to compliance and security issues.
You should also review your vendor relationships regularly to confirm AI tools meet security and compliance standards. Lastly, establish feedback loops to monitor AI performance and refine your models over time to maintain effectiveness and alignment with business goals.
5. Address Change Management and Human Adoption
Introducing AI is as much about people as it is about technology. You’ll need to educate your leadership and staff on AI’s potential benefits to address — and quell — any fears about job displacement or complexity. Position AI as a tool to enhance, not replace, human work and explain how their roles might shift to focus on strategy, quality assurance, and decision-making.
Part of your work in change management will also include training. For AI to be an effective addition to your financial services organization, your team members need to understand how to use it properly — or else, the experiment has a real risk of failing. It will also help your teams feel confident and capable as AI becomes a larger part of their workflows.
The Path Forward for AI in Financial Services
AI is no longer simply a fraud detection and risk management tool — it’s a transformative force reshaping customer engagement, compliance and back-office operations in financial services. By identifying high-impact use cases, starting with small pilot projects and establishing robust governance, you can strategically integrate AI to drive efficiency, enhance compliance, and improve your customers’ experiences.
The key to success lies in balancing innovation with practicality. Embracing change management and ensuring team members are equipped to work alongside AI will ensure adoption and long-term effectiveness. With thoughtful implementation, AI can empower your financial services organization to operate smarter and deliver greater customer value.
If your organization is ready to explore the possibilities of AI, we can help guide you through each step, working with you to unlock its potential and future-proof your operations.
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