10 Financial Services Use Cases That Demand AI
Powerful AI technology can improve customer service, fraud detection, onboard customers, uncover credit risk, and more.
Automation and Artificial Intelligence (AI) go hand in hand. But when you add in massive amounts of data, they power productivity and augment services and experiences for customers and employees.
With financial services companies having access to so much data and seeking ways to gain a competitive advantage, they’re turning to AI technologies. In doing so, they’re automating processes, creating efficiencies, gaining more accurate insights, and delivering personalized customer experiences.
Keep reading to learn about the current state of AI for financial services companies. Then, discover the benefits of AI and 10 ways you can put AI to use and take your organization to the future.
The current state of AI in financial services
In a February 2022 survey by AI Forum, over 640 senior business and technology managers of financial institutions gave insights into the current state of AI for financial services. Among the respondents, primarily across the US, Europe, and Asia Pacific, 61 percent indicated they use or plan to use AI for some type of decision-making.
As financial services companies implement AI, they’ll push toward automated decisioning with a focus on:
Developing a full network of relationships and transactions to gain a holistic view of customers and their counterparties as an input to AI models.
Building AI models that thrive on better context by combining internal data, transactional data, and external data to provide a networked single view of the customer.
Ultimately, providing more accurate and personalized decisions with customers to gain a full competitive advantage.
At the heart of automating decisioning is data readiness—how an organization prepares its data to be reliable and relevant data sources for AI platforms.
Benefits of AI in banking and finance
The benefits of AI for financial services center on automation, optimization, and detection.
Reduced time-intensive and resource-intensive costs. AI automates banking and finance processes by digitizing paper-heavy manual tasks. For example, some banks are using robotic process automation (RPA) to enter customer data from contracts, forms, and other sources. As a result, it significantly reduces the staff and time to complete these processes, as well as minimizing any associated errors.
Better customer experience. By using AI, financial services can be where their customers are when they need them. With AI-powered chat bots, customers get answers to their questions and carry out standard banking tasks 24×7. Meanwhile, banks can program their chat bots to upsell customers on their services. As a result, financial companies deliver personalized customer service and perhaps even grow revenue.
Advanced fraud detection. As financial services apply various algorithms to large amounts of good data, they’re able to detect inconsistent consumer patterns. This level of detection is especially helpful when a cardholder makes a purchase in one city, but someone fraudulently uses their stolen information in another location. The ability of AI to detect such incidents saves both financial services and consumers thousands, even millions, of dollars in fraud cases.
Improved regulatory compliance. To meet government regulations, financial services must know their customers, maintain customer privacy, watch wire transfers, and prevent financial crimes, such as fraud, money laundering, and theft. They use AI to monitor transactions and customer behaviors, as well as audit and track information to regulatory systems. As with fraud detection, this level of compliance isn’t possible without a high volume of good, well-structured data.
Refined loan and credit decisions. Banks are turning to AI to help guide them in making informed, safer, and profitable decisions related to loans and lines of credit. With AI-based loan decision systems, they can identify behaviors and patterns about customers. For example, they might discover that someone with a limited credit history might, in fact, be a good customer to take on. Or they might find that a customer with risky credit patterns might default on a loan.
Automated investment research and guidance. Some financial companies are using AI to assist with investment banking research and investment decisions. While their employees are still part of the process, these companies can leverage AI to reveal additional options from improved modeling and discovery. They might also use AI as robo-advisers—based on personalization, chatbots, and customer-focused models—to help clients with portfolio management. Using chatbots makes this service available when and where customers need assistance.
10 financial services use cases that demand AI
Financial services that have adopted AI are primarily using it for Know Your Customer (KYC), anti-money laundering (AML), and fraud detection operations. According to the AI Forum study, of the respondents who are planning or are already using AI, 28 percent of respondents indicated adopting AI for these areas. Another 28 percent are dedicating AI to data management, along with customer insight and intelligence.
See how financial institutions worldwide use AI to re-invent their operations.
Data management. The financial services industry has a plethora of big data. To help manage all that data, banks and financial companies are using AI to help structure their data and generate actionable insights, transforming their business intelligence. AI enables these companies to automate data matching, entity resolution, network generation, analytics, visualization, and exploration.
Customer insight and intelligence. Financial services are using AI to help them understand their customers and how they can better align their lines of business with them. AI enables them to make suitable product recommendations and engage customers more effectively based on a combination of personal data, credit information, and account balances.
Fraud detection. While traditional monitoring approaches can prevent fraud, they require AI to outsmart fraudsters. By using AI models, they can analyze multiple data points in real time to detect anomalies and suspicious or fraudulent activity. They get more accurate fraud alerts and a broader picture that explains the attack.
Onboarding customers/Know Your Customer. Onboarding customers with AI automates time-consuming, manual verification processes. With AI-based technologies, financial services get digital identity verification, AML screening, and transaction monitoring. In fact, AI-powered KYC solutions can run spoofing and Photoshop tests to verify whether a document had been altered. In that regard, they also help prevent scams and potential criminal activity.
Risk decisioning. Searching for a way to offer loans with less risk of bias, banks are using AI to guide decisions around consumer credit. By using AI, they can more accurately predict the type of risk a customer would pose, regardless of race, religion, gender, and other human biases.
Business development. Financial services are also turning to AI for more effective business development. They are using it as part of their marketing, forecasting, selling, sales pipeline, and customer service strategies. In doing so, they’re able to analyze vast quantities of data and make logical decisions—decreasing human involvement, reducing operational inefficiencies, and increasing productivity.
Compliance, surveillance, and anti-money laundering. As government regulators push for stronger, efficient compliance, banks are seeking more cost-effective solutions to keep up. By using AI-powered data analysis, companies can develop integrated risk and reporting systems. Also, machine learning can help manage data-quality issues for trade repositories, increasing the value of the data to both government regulators and consumers.
Customer relationship management and customer service. Banks and finance companies are using AI to power intelligent chatbots. The around-the-clock availability of these chatbots enable banks to improve their customer engagement and communication by providing personalized service and offers. For these companies to achieve greater accuracy and success, the AI algorithms must be able to process both big data and interactions.
Operational optimization. Because AI algorithms can handle large amounts of data, they create efficiency, deliver accuracy, and speed calculations to optimize time-consuming and resource-intensive operations. As a result, they nearly eliminate manual processes and save on personnel.
Governance and risk. AI and big data together enable financial companies to centralize risks across their business, strengthening their overall risk management. AI can also leverage data to identify trends and profitable trades that are undetectable by humans. And by using AI algorithms, financial services companies can scan news, metadata, or both for features that correspond to the events their customers are interested in.