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AI is poised to be the next disruptor for banking products and services, and banks that can leverage the power of artificial intelligence stand to earn their share of an estimated $1 trillion in savings over the next 10 to 15 years. Put your bank on the right path by following the three stages of successful AI implementation. 

1. Identify your use cases

The first stage of successful AI implementation is to identify your use cases; that is to say, how artificially intelligent solutions can improve your bottom line. The top four uses cases for AI in banking are: 

  • Improved risk management and compliance 

  • Enhanced customer experience

  • Advanced operational efficiency

  • Revenue growth

Let’s examine how each area can work for your bank.  

Improved risk management and compliance

AI-driven applications power smarter, faster fraud detection that minimizes risk and ensures compliance. Sophisticated algorithms can instantly scan millions of transactions to detect potentially fraudulent credit card purchases. They can also automate Know Your Customer (KYC) and Anti-Money Laundering (AML) compliance, eliminating tedious and error-prone manual processes that can’t meet tightening regulations. 

The same tools can be employed to make stronger credit and underwriting decisions. When a potential borrower submits a loan application, for example, computers can analyze data to help bankers determine whether the applicant is likely to meet the terms of the requested loan or default. Even non-traditional data sources such as borrower education and job history can factor into the equation, resulting in better decision-making devoid of human error and ultimately improving lending success. 

Such technologies have a direct impact on your bottom line. In fact, analysts estimate that FIs could save up to $217 billion through risk, compliance and authentication projects over the next 10 to 15 years.

To recap, your bank can harness the power of AI to improve risk management and compliance via: 

  • Stronger fraud detection

  • Automated KYC and AML compliance

  • Data-supported credit and underwriting decisions 

Enhanced customer experience

Artificially intelligent platforms can strengthen relationships between your bank and both retail and commercial customers. They employ technologies such as natural language processing (NLP) and image recognition to reduce “friction points” when customers interact with banks.  

For example, NLP-powered chatbots can hold conversations with customers on your banking platform. They can answer basic questions, help customers find additional resources and even tap into a neural network to deliver personalized offers, cross-sells and upsells (more on that later). 

Virtual assistants can help customers self-service their accounts and eliminate routine and redundant tasks that otherwise require staff time. Thus, a customer can use an interface like Siri or Alexa to check their account balance, find the nearest ATM or transfer money from one account to another.  

Biometric and facial recognition eliminates the need for passwords and offers greater account security, which reduces the need for staff to spend time helping customers reset their passwords and eliminates customer frustration by ensuring account access. 

With AI-driven solutions, customers can get the help they need faster than ever before – all without requiring a single minute of staff time. That, in turn, allows staff to gain deeper customer insights and focus efforts on building customer relationships and other valuable activities.  

In fact, organizations that implement AI for customer service report up to 70% fewer calls or email inquiries and a 33% savings compared to calls with live agents.

Your bank might be interested in these customer enhancements afforded by artificial intelligence:  

  • Account self-service

  • Chatbots 

  • Virtual assistants

  • Biometrics and facial recognition 

Advanced operational efficiency

Streamlined processes and automated workflows offer massive savings for banks, and these initiatives are at the heart of many AI implementations. One challenge many banks face is unstructured data handling, and that challenge can be met through next-level robotic process automation (RPA) called intelligent automation (IA).  

Sophisticated artificial intelligence allows for computers to automatically match incoming electronic and paper payments to open invoice remittance details from accounts receivables processing systems. These platforms can scan and identify unstructured data trapped in PDFS and emails, then place that data in appropriate spreadsheets, reports and software. This, in turn, eliminates human error and frees staff to focus on more valuable activities. 

In fact, AI-driven solutions can handle contract reviews, reporting and workflow automation, ultimately resulting in an estimated $200 billion in savings for financial institutions over the next 10 to 15 years.  

To recap, your bank can employ artificial intelligence for advanced operational efficiency via: 

  • Contract reviews 

  • Reporting

  • Straight-through processing 

  • Workflow automation

Revenue growth

So far, we’ve discussed how artificial intelligence can save your bank money. However, AI offers exciting opportunities to make money, too.  

Remember chatbots and virtual assistants? These technologies are powerful ways to streamline operational efficiencies and offer customer support, but they can also be employed to drive revenue growth. 

Many banks deploy pop-ups and banner ads on their customer platforms to promote products such as mortgage refinances, auto loans and small business loans. Those types of ads are hit or miss, since you can never be certain you’re serving them to targeted customers.  

Through the power of machine learning and automation, chatbots and virtual assistants can deliver personalized promotions, cross-sells and upsells to customers who meet targeted criteria and are therefore most likely to act. 

For example, let’s say you have a customer who often makes international wire transfers. When your customer logs in, he might be greeted by a chatbot: 

Joe Treasury, I see you sent five international wires last week. Did you know other electronic payment options are available at less cost?

Naturally, the customer is far more likely to respond to that prompt than click a banner ad. The chatbot can then instantly sell a new product or refer the customer to a live banker for a deeper conversation. In fact, organizations that implement AI for sales report a 30% higher conversion rate with prospects.  

That’s not the only way artificial intelligence can grow revenues, either. Your platform can continually monitor customer behavior and identify red flags that indicate a customer might leave your bank. It can then notify a banker, who can contact the customer, find out what the problem is, save the account and stem attrition. 

Robo-advisors can be deployed to help customers make important financial decisions based on their circumstances, market trends and other information – all without human intervention. They can make accurate product recommendations, influence sales and notify bankers when customers are ready to act.  

Finally, artificial intelligence enables banks to bolster their product offerings. Remember how Integrated Receivables (IR) solutions can employ sophisticated algorithms and machine learning technologies to match customer invoices with electronic remittances? That service can prove a compelling product that adds value to your bank’s relationship with corporate customers. 

With AI, your bank can grow revenues via:  

  • Personalized offers

  • Upsell and cross-sell recommendations 

  • Robo-advisors

  • Alerts for at-risk customers 

  • Integrated receivables

Add it all up, and experts predict that artificially intelligent solutions can save banks $1 trillion by 2030.  

2. Ready your organization

Once you’ve identified use cases for AI in your bank, the next stage is to prepare for implementation. Successful implementation relies on your bank’s adherence to four key strategies:  

  • Make AI cross-functional

  • Start with business needs 

  • Plan for quick wins

  • Build a digital culture 

Let’s take a closer look at each strategy and what it means for your bank. 

Make AI cross-functional 

Though technology is typically the domain of IT departments, AI solutions serve all bank departments and therefore should not reside exclusively within IT. The ability to coordinate and collaborate between departments is critical to successful implementation, so early adopters recommend creating an AI Center of Excellence (CoE). 

The CoE operates similarly to a shared service model. Staff members establish the vision for AI, and personnel roles and procedures are clearly defined for each department. Departments collaborate as a team to ensure AI meets both departmental and organizational goals. Internal and external resources as well as success stories are shared systemwide.  

The goal is to make AI work for everyone in your bank and define a path to success: automated workflows, cost-savings and revenue growth. 

Start with business needs 

Successful organizations prioritize business and end-user needs first. They don’t try to harmonize every aspect of data before moving forward and they don’t worry about being “perfect” right out of the gate. 

Instead, they address data architecture and quality issues incrementally, which allows them to make simultaneous progress on AI. That way, both data and AI tools improve over time.  

What does that mean for your bank? Perfectionism will hold you back. You’ll spend time and money trying to build the perfect system, delay implementation and likely fail. However, if you start with business needs and address them incrementally, you’ll realize success much faster and can deploy additional successful AI solutions over time. 

Plan for quick wins 

Your bank’s budget cycle probably spans anywhere from a few months to a year, but full-blown AI development can take multiple years to implement. That’s because the technology learns first with human supervision before it learns on its own. The takeaway is your bank should focus on smaller initiatives that can deliver quick wins within a single budget period. 

Identify use cases that can deliver immediate value, then launch pilot projects. These are minimum viable products that are just functional enough to demonstrate AI’s full potential.  

Pilots make it far easier to deploy AI solutions rapidly, convince senior leaders about the value of AI and secure funding for larger projects. 

Build a digital culture 

Recognize that AI brings new roles, processes and ways to collaborate to your bank. It represents a new way of doing business, and it’s possible you’ll face internal opposition if you don’t work to establish a digital culture predicated on collaboration. 

This goes beyond creating a CoE. Your bank should develop new roles to facilitate the transition and ensure artificially intelligent solutions work for everyone. Important roles include a “translator” who serves as a liaison between business and technical stakeholders and an “evangelist” to champion AI projects bank wide. Some companies add an “ethicist” to measure the impact of AI on consumers and evaluate potential bias in machine learning.  

You’ll also need to create staff roles to manage data ownership, governance, quality and technology. It cannot be understated how critical the right leadership is to successful AI implementation, so identify visionaries within your bank who can not only lead, but also help establish a digital culture across your organization. 

3. Find the right partners 

There’s no single path to successful AI implementation, though most banks leverage a combination of internal talent, external resources and off-the-shelf AI components. Here are three paths to consider: 

Internal hires 

AI is experimental and ongoing, so if you hire internally you must be open to change. The field attracts curious problem-solvers who excel at math and data analysis, but it’s important to understand the differences between experience levels and job titles. Though it’s a booming field, even the largest banks struggle to fill AI roles and the right candidates typically command high salaries. Often, the investment isn’t a good fit for mid-sized and small banks. 

Turnkey solutions 

Turnkey AI solutions are already developed and available to be deployed immediately. You’ll still need a technical partner to implement turnkey solutions, but you won’t need to invest in custom development. 

Third-party partners 

Outside resources such as technology partners and consulting firms offer expert solutions without the need for internal hires. They often attract top talent, understand the specific needs of the financial industry and have experience implementing AI solutions within banks. They can develop custom platforms that work seamlessly for your bank or integrate turnkey solutions. You can work with a partner on an ongoing basis or on single projects, which allows you to control your investment and timing. 

Most large banks get help from third-party AI resources, then gradually evolve to in-house leadership roles (though they still rely on outside experts for new and large-scale projects). However, partnerships might be the best opportunity for regional and community banks. That’s because partnerships enable you to retain your core focus on customers and financial services, while your AI partner complements your strengths with cutting-edge skills and technology.

How to choose the right external partner

Outside partners offer innovation and agility that speeds development and streamlines processes. They also make it easy to scale without the need to reshuffle internal staff from project to project. 

Still, not all partners are created equal, so it’s important to vet potential partnerships before you take the plunge. These tips will help you identify the right technology partner, consulting firm or AI development agency. 

Create a scorecard 

Create a list of the most important criteria for your financial institution. Research potential partners and rank them according to criteria such as industry knowledge, experience, available functionality and customer service. Look beyond price and hype to make a shortlist of good fits. 

Vet their expertise

Interview each candidate to learn more about their capabilities and experience. Ask for references and review their websites as well as their clients’ websites to identify the very best candidates for your bank. 

Assess cultural fit 

Experience and capabilities are critical, but you also need to determine whether a potential partner is a good cultural fit for your bank. Explore shared values and evaluate each candidate on trust and communication. Meet their teams and determine whether their personalities and work styles mesh with your team. 

Identify risks

Risk is inherent in technology projects. Understand potential risks and plan to meet the challenges they present. Identify what risks each candidate carries, whether it’s skills-based, reputational, financial or regulatory. 

Don’t be afraid to reach out to third-party resources and ask questions. You need to know if a potential partner can deliver within your timeframe and on-budget, how many customers they work with simultaneously and who owns the end project. How will ongoing bugs and maintenance be managed? Are these accounted for in your budget? Who is responsible for ensuring regulatory compliance?  

With due diligence, you’ll be able to identify the perfect partner for your bank. It’s a critical step toward implementation: as the next disruptor for FI products and services, banks that can harness the power of AI stand to make major gains over the next 10 to 15 years. Follow the stages outlined here to create a well-planned strategy that leads to successful implementation of AI within your bank.

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