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Are you facing the challenge on how to implement AI in the Banking and Fintech sector successfully?
Chris Brown, President USA of Intelygenz, our AI and deep-tech dedicated solutions company from VASS, highlighted five successful strategies for implementing AI in the banking and fintech sector in an article published in Fintech Magazine.
For Brown, the integration of AI in Banking goes beyond innovation. Thanks to AI, financial profitability can increase significantly, achieving tangible returns on investment.
To achieve this, there is a problem to solve. 85% of AI projects do not reach the production phase. Therefore, there is a real challenge in moving from hype to ROI, with realistic projects that truly make a difference.
Properly implemented AI is a clear ally. As Brown states, "it can solve complex commercial, operational, and economic challenges, directly impacting the company's results."
Thanks to the five strategies we are about to see, AI projects will not only reach that 15% of successful projects that go into production but will also deliver a significant ROI that helps to:
- Build a leading data-driven architecture.
- Automate daily operations.
- Deliver improved solutions focused on the human experience.
1. Prioritize Practical Solutions Over Theoretical Models
For an AI implementation to be successful, the focus must be on practicality. Immediate implementation solutions should be sought rather than concentrating on theoretical models.
A clear example is the integration of risk assessment tools in loan approval processes. AI enables the reduction of defaults and increases the processing speed of applications, thereby improving profitability.
AI should be focused on real problems, providing clear and measurable returns on investment.
2. Foster Collaborative Development
Secondly, AI tools need to adapt to the professionals who will be using them. It's not just about finding technologically robust tools, but they must also be tailored to the specific needs of the financial sector.
To achieve this, the various professionals they will serve must be taken into account.
3. Implement Rapid Prototyping and Iterative Testing
Thirdly, the speed in implementing prototypes and conducting various iterative tests is key to getting them fully operational as soon as possible. This way, AI can adapt to real-world conditions.
Reducing time to market also lowers operational costs and economic pressures.
"By focusing on production-ready AI that delivers tangible ROI, organizations can transform these challenges into competitive advantages. Intelygenz, a VASS company with its robust methodology and a decade of experience in deploying successful AI solutions, proves to be an invaluable partner in this journey. By aligning AI capabilities with strategic business outcomes, we ensure that technology investments generate measurable and significant returns in weeks, not months or years."
4. Focus on Compliance and Security from the Start
Fourthly, the development phase should never bypass compliance, even in the early stages. Regulatory requirements are crucial in the financial industry.
By doing so, we ensure from the outset that AI solutions are compliant and secure, avoiding fines, reputational damage, protecting the entity's economic interests, and enhancing consumer trust.
5. Leverage the End-to-End Expertise
Lastly, Chris Brown points out in his article that an End-to-End expertise "is vital to ensure that AI projects not only launch but are also continuously optimized to deliver ROI."
This involves all parts of the project, from system integration to real-time performance monitoring. In this way, AI solutions continuously adapt and evolve as the financial entity's needs change.
Navigating the complexities of AI deployment in this sector requires not only technological innovation but also a strategic approach that considers the sector's specific challenges, both commercial and operational.
Chris Brown
President USA
Intelygenz, a VASS company