Blog Financial Institution
The financial industry is abuzz about artificial intelligence (AI) and how it can be leveraged to reduce costs and increase revenue. Find out exactly what artificial intelligence is and what it means to midsize banks in this FAQ.
A: AI refers to several techniques that enable computers to emulate human behavior. These can include machine learning, intelligent automation, natural language processing, robotic process automation, chatbots and virtual assistants.
A: Machine learning is the process of getting a computer to act without being specifically programmed. The computer learns by experience without human interaction.
A: The power of AI stems from its ability to harness your bank’s data in three key ways: to analyze, to act and to improve by self-learning. Financial institutions that employ AI can improve the customer experience, increase operational efficiency, gain new insights from previously trapped data, and bolster fraud detection and risk management.
A: AI toolkits are typically comprised of the following elements:
Algorithm: Simple rules and step-by-step instructions that instruct computers on how to solve problems
Neural Network: Helps a computer develop human-like functions such as perception, reasoning, visual recognition and language processing
Deep Learning: The activity that occurs when data and inputs pass through the neural network. By passing data through layers of processing, the computer self-teaches itself by finding patterns to improve its performance
Supervised Learning: A method of “training” a computer by providing it with both the question and the answer to a problem, so it begins to learn on its own
Unsupervised Learning: Providing the question without the answer, so that the computer must use advanced calculations to learn on its own
These technologies are used to build AI applications that can be employed in financial institutions, such as:
Natural Language Processing (NLP): Training a computer to interpret and respond to human text and speech
Image Recognition: Training a computer to identify objects, places, people and handwriting
Chatbots: Programs that use NLP to hold conversations with human users
Virtual Assistants: Applications that help humans complete common tasks, such as with iPhone’s Siri and Amazon’s Alexa
Robotic Process Automation: Software designed to handle routine business processes, such as collecting data and updating spreadsheets
Intelligent Automation: An advance form of RPAs that not only handles routine business processes, but learns how to do them better
A: Of course! By 2030, analysts predict AI in the financial services industry could:
Yield up to $1 trillion in productivity gains
Increase revenue by 35%
Reduce operating costs by 22%
Result in a 14% net gain in jobs
If that’s not reason enough to integrate AI into your bank, consider that FIs could collectively save $217 billion through risk, compliance and authentication projects alone – plus an additional $200 billion through increased back office efficiencies.
A: AI in banking falls into four investment categories: risk management and compliance, customer experience, operational efficiency and revenue growth. We’ll cover specifics on each below.
A: By improving outcomes in three areas: fraud detection, KYC and AML compliance, and credit and underwriting. Here are some examples:
Fraud detection: Credit card processors use sophisticated AI algorithms to scan millions of transactions and detect fraudulent purchases
KYC (Know Your Customer) and AML (Anti-Money Laundering) compliance: AI-driven applications extract data from a variety of sources during onboarding to lend greater insight into who your customers are; the same tools can be used to examine millions of transactions and quickly flag suspicious activity
Credit and underwriting: AI-driven applications can extract data from traditional and non-traditional resources to offer an accurate customer snapshot that helps banks make sound consumer credit decisions
A: By making it easier for customers to service their own accounts, improving customer security, and providing instant customer support. Examples include:
Account self-service: Natural language processing (NLP) enables customers to manage their accounts remotely, even by voice via devices such as Amazon’s Alexa
Improved security: Advanced facial and biometric recognition replaces password-based authentication, strengthening account security
Customer support: Chatbots and virtual assistants eliminate “friction points.” They make it easy for customers to get instant answers to questions make important account changes without tying up customer service staff or the need for face-to-face interaction
A: Yes! In fact, organizations that implement AI for customer service or sales support report greater overall customer satisfaction. To quantify that, AI results in up to 70% fewer calls and email inquiries and 30% greater sales conversion rate – all at a 33% savings compared to a call with a live agent.
A: AI helps banks streamline processes and workflows for greater efficiency. One example is unstructured data handling: AI can increase straight-through processing rates by up to 95%. AI-driven applications can also automate contract reviews and reporting.
A: AI empowers FIs to increase revenue through a suite of sophisticated processes. They include:
Personalized offers: AI utilizes data to deliver targeted marketing based on customer behaviors such as spending habits and travel locations, which can dramatically increase conversion rates and ad ROI
Upsells and cross-sells: Similarly, AI can automatically recommend upsells and cross-sells based on customer data
Robo-advisors: AI can examine records, trends, customer behavior and market data and process it to offer customers sound financial advice they can “take to the bank”
Alerts for at-risk customers: AI-powered tools can monitor customer behavior and provide early warning when high value-customers are at risk for attrition, such as when customers decrease usage of the bank portal, so customer service staff can act fast and improve customer retention rates
A: Hardly! Rather than replace bankers, AI seeks to automate redundant tasks. It also provides tools bankers can use to better understand their customers. That means staff members can focus less on helping people check their account balances and more on complex interactions that drive revenue, like customer acquisition and upselling.
A: Yes, and the sooner, the better. While more than 70% of large banks have implemented AI for front- or back-office functions, just 2% of midsize banks have taken similar initiatives. Moreover, only 13% of midsize banks plan to invest in AI in the near future, and more than 50% say AI isn’t even on their radars.
That spells bad news for midsize banks that refuse to adopt AI technology. The longer they wait, the greater the competitive gap.
A: Falling behind, and fast. Not only will large banks attempt to steal market share from midsize banks and credit unions, smaller financial institutions that employ AI will have a major advantage now and in the future.
There’s no reason you can’t compete with larger banks; in fact, many customers prefer the sense of community they get from midsize banks and credit unions. But AI makes it easy to reach the right customers, with the right offers, at the right time – a powerful path to growth.
Adopting AI now puts your bank in control of its own destiny, allowing you to compete with larger banks and quickly outgrow similarly-sized competitors who have not yet – or will never – adopt AI.
A: AI can make immediate improvements in revenue and operational efficiency, but a full-blown AI integration can take years. That’s because AI technology learns first with human supervision, then on its own. Over time, your bank will evolve to take full advantage of AI’s potential. In the meantime, solutions can be implemented in phases to keep banking operations flowing smoothly.
A: You don’t need in-house talent to integrate AI into your financial institution. All you need is a partner who understands how to transition FIs into an AI culture. In fact, most large banks started their AI journey with third-party resources. Some have since brought AI in-house, but even those often rely on outside experts for consulting and special projects.
A: Great question! Start by identifying exactly what resources your financial institution needs, then:
Create a scorecard to rank potential partners based on knowledge, experience, functionality and customer service
Screen candidates via phone interviews and referrals
Access cultural fit to determine whether you share the same values and various personalities mesh with your team
Identify risks, including reputational, financial and regulatory risks, to gauge your confidence level in each candidate
A: Yes, when used correctly. That said, AI ethics are an important consideration for many financial institutions. That’s why some organizations have hired “digital ethicists” to measure the impact of AI on consumers and evaluate potential bias in machine learning. Choosing a partner who is in tune with the ethical implementation of AI is critical to protecting your customers and maintaining your financial institution’s standards.
A: Developing a strategy that emphasizes quick wins and cross-functional collaboration is the first step toward successful AI integration. Let’s break down what that means:
Cross-functional AI: AI should be integrated organization-wide, but there can be conflicts and challenges to organizational overhaul. That’s why early adopters recommend creating an AI Center of Excellence (CoE) that’s independent of other departments and can foster internal collaboration
Start with business needs: It seems like AI can do anything and everything, but trying to do too much too soon can impede successful integration. Instead, prioritize your most critical needs, then scale your AI integration
Plan for quick wins: Look for easy ways to implement AI for quick wins via minimum viable products – those that have just enough functionality to demonstrate the full potential of AI. This practice not only smooths the transition to AI, it helps secure buy-in and funding from senior leaders
Build a digital culture: Understand that AI brings new roles, processes and ways to collaborate to your financial institution. Assign leadership roles to “translators” who can connect business and technical stakeholders and to an “evangelist” who can champion AI projects bank wide. You also need to consider the ethical impact of AI on customers and how staff will need to manage your data and technology
A: Start by learning more about what AI can do and how other banks have employed AI to cut costs, grow revenues and streamline operations. Speak with your staff about current inefficiencies to identify problem areas AI can help with. Brainstorm a list of tasks that, if automated, would free your staff to focus on revenue growth initiatives versus repetitive tasks. List the most common, yet easy to answer, questions customers have and consider how much time your bank would save if your customers could get the answers without the need for human assistance.
Next, list your concerns. What are the challenges of adopting AI? What are the risks? In other words, what are the pros and cons?
With your lists in hand, begin reach out to potential partners. They might have solutions you haven’t thought of; but most importantly, you want to see if they can solve your problems and present a clear path to successful AI implementation. Start a dialogue and vet candidates carefully to identify a partner who can help you leverage the power of AI to not only compete, but thrive and scale in the new world of banking.
Blog Financial Institution
Blog Financial Institution