Congratulations! Your bank is now a tech company.

Mastering your AI project means aligning the right resources.

Resources. They’re one of the biggest decisions your bank will make in its artificial intelligence (AI) program. In-house or outsourced? Fintech or consulting firm? Ongoing relationship or project-based?

There’s no single path to success. Most financial institutions (FIs) will leverage a combination of internal talent, external resources and off-the-shelf AI components.

Fintech partnerships ideal for regional banks

Large, global banks with AI-driven solutions already in the market typically achieved their progress with the help of third-party resources. Now, as the AI discipline matures within their organizations, they’re moving toward in-house AI leadership roles—while still relying on Fintechs and other outside experts.

For regional and community banks, partnerships may offer the best opportunity. You retain your core focus on customers and financial services. At the same time, you complement these strengths with best-of-breed partners that bring cutting-edge skills and technology—everything from machine learning platforms to natural language processing experience.

Banks and Fintechs are learning to co-exist rather than compete, making for smart collaborations. Where banks are slowing evolving away from paper and legacy systems, Fintechs were “born digital.” They bring an innovation mindset and an agile approach that can speed development and streamline processes.

Working with outside resources also makes it easy to scale. You can rapidly jumpstart a project with a resource push, then reduce the workload as your AI project matures. Using an external firm eliminates the ebb and flow on your bank’s internal staff.

Weighing your options

If you hire internally, be open to change. AI relies on data, but it’s also experimental and ongoing; AI attracts those with innate curiosity, perseverance and problem-solving, as well as strong mathematical skills. When hiring, don’t fixate on someone who fits the current mold, or follow the same playbook used with software developers.

It’s also important to understand the experience level you need and the differences in AI job titles. In this booming field, high-end talent with financial services expertise can be costly. Know your requirements and where you’ll be willing to invest in on-the-job training, so you can attract the best talent for your budget.

Ask these questions to determine your resource needs:

• Does a turnkey AI solution already exist?

• What is most essential in a custom solution?

• What are the top skills needed?

• Which should we staff in-house? With external resources?

Once you agree on your direction, follow these best practices to choose the right Fintech, consulting firm or AI development agency.

Evaluate your Fintech resource options

Create a scorecard. Identify the most important criteria for your FI’s decision, then rank each resource accordingly. Common elements include industry knowledge, past experience, available functionality and customer service. This helps you focus your decision on more than price—or hype.

Vet their expertise. Start with a shortlist of potential partners, then conduct phone interviews to screen the best candidates. Do your due diligence with website reviews and references as well. Important questions include:

• Describe how you solved a business challenge similar to ours?

• Which AI methods and platforms are you most versed in?

• How do you typically work and collaborate with your clients?

• Describe how your AI solution typically “learns”? How much time and training are needed to see performance improvements?

Assess cultural fit. Sometimes, a relationship looks good on paper, then falls apart in practice. Don’t underestimate the importance of shared values between your bank and your AI resource. Trust and good communication are crucial, especially in a high-visibility project. Take time to meet the team and make sure personalities and work styles mesh.

Identify your risks. Every technology project carries risks; the goal is to understand their nature and plan accordingly. With outside partners, risk can be skills-based, reputational; even financial or regulatory. Get clarity around your vendor’s capacity—can they execute your project within the desired timeframe? How many customers do they work with simultaneously? With a custom project, be clear on who owns the end product, as well as ongoing bug fixes and maintenance. Lastly, in a regulated industry like financial services, agree on the responsibilities for compliance to avoid costly missteps down the road.

No matter how you resource your AI project, good communication and upfront planning will smooth the way.

This content is accurate at the time of publication and may not be updated.