AI and The Future of Finance: Opportunities and Challenges (4 of 4)
From customer experience enhancement to fraud detection and beyond, artificial intelligence (AI) is impacting the financial services industry in ways we haven’t seen before.
The question is no longer IF your organization will adopt AI into it’s business operations, but WHEN and HOW this deployment will take place.
The WHEN is becoming 2025 for most enterprises and banks in finance.
From our past dialogue in this series, there’s no switch that ‘flips ON’ when it comes to adopting AI.
The transformation requires a heavy lift from senior leaders in terms of strategic planning, investments, sourcing capacity and staffing, and responding to ethical concerns.
In the finale of our 4-part series discussing AI in Financial Services, we cover a:
Summary of the opportunities from AI;
Top challenges in applying AI as an organization (and how to respond);
What’s ahead in 2025 (and beyond) for AI in Finance.
Recap of AI Opportunities in Financial Services
The common areas in which artificial intelligence benefits companies tend to be the same — automation and data insights.
Within banking & finance, AI’s application is becoming a true ‘gamechanger’ for financial institutions and enterprises. Here are the specific functions being innovated today:
Hyper-Customization of Finance Solutions
Through AI, teams are able to craft nuanced products based on actual user data/behaviors — in real-time.
Models enable segmentation by customer habits/needs, which result in unique programs that boost loyalty.
A leading example comes from investment strategies: financial institutions leverage AI and customer inputs (goals, risk preferences) to deliver recommendations for investment.
Automating of Repetitive Processes
Operational tasks (in banking) are ready for automation via AI solutions — top areas include: user onboarding, transaction monitoring, credit underwriting, and dispute resolution.
Through AI, the speed AND accuracy for high volume reviews greatly improves (in comparison to manual review).
Capacity saved from automation allows employees to focus on activities with greater impact to their organization — especially customer-facing responsibilities.
Improving Financial Inclusion
Globally, there are numerous groups who are underbanked (no access to deposit account, or credit/loan products) due to bank requirements and fees.
In utilizing alternative data (e.g. educational degree, payroll history, etc.), banking platforms are able to gauge credit standing without the use of traditional scores/history.
For inclusive-minded organizations, AI can expand the scope of potential clients and support underserved communities.
Enhancing Fraud Prevention & Detection
The data analytics from AI can quickly detect outlier activity from fraudulent transaction requests.
Pattern recognition and deep learning (from machine learning algorithms) power the latest risk management solutions in responding to real-time fraud attempts.
By detecting multiple flags at once, AI is also reducing false positives (in which actual users are conducting transactions).
Increased reliability with Risk Management Programs
As discussed in Part 3, AI offers powerful tools for risk assessment and mitigation (beyond fraud detection).
Companies can become proactive in their approach to risk by utilizing predictive analytics to forecast future threats — creating a more stable, compliant environment.
These five opportunities lead to additional capacity for companies, gains in customer satisfaction, reduction in losses (from fraud), and new revenue streams.
However, there are blockers that organizations must consider before incorporating (or building) a new AI solution.
Key Blockers for Orgs Exploring AI (and How to Respond)
The top challenges stem from AI solutions being integrated in an ethical and compliant manner.
Resources and investment would be wasted if AI-powered products cause missteps with regulation, privacy, or ethical issues.
Here are the hurdles that come up most often:
Ethical Concerns, Bias: Data inputs for AI models determine the level of bias present. If historical data sets contained biased data, a new solution would expand it even further. Application screenings (for loans, employment, account approvals) are sensitive processes that can be negatively impacted by bias.
Response: Organizations should require ethical guidelines for AI deployment, which focus on: (i) fairness, (ii) unbiased training data, and (iii) periodic reviews of output decisioning;
Regulatory Adherence: Banking & finance are part of complex regulatory frameworks that continue to evolve each year. Industry concerns (such as privacy, liability, and transparency) are exacerbated by AI models with ‘black boxes’ (i.e. unknown criteria for decisioning).
Response: Industry groups should actively collaborate with regulatory agencies so that AI guidelines for orgs are crafted to embrace compliance, clarity, and innovation.
Privacy & Security: Based on high volumes of data flow (including personal user details), AI solutions have higher cybersecurity risks. In an industry in which customer trust is necessary, avoiding privacy breaches (and other threats) is critical. .
Response: Organizations must prioritize a robust cybersecurity solution that includes: (i) real-time threat detection, (ii) recurring assessments for vulnerabilities (such as penetration testing), and (iii) the addition of encrypted controls.
Legacy Systems Add Complexity: Outdated infrastructure is still prevalent in many financial institutions, which increases challenges with integrating the latest AI solutions.
Response: Shifting to cloud-based, API enabled tech stacks would allow enterprises and banks to adopt AI-powered programs.
Talent Shortages: Skilled professionals with AI expertise are needed to implement the latest products & services. However, the low supply places a premium on existing talent — creating a staffing (and affordability) gap globally.
Response: Organizations should develop training camps/programs to create a new talent pool. Companies can partner with colleges and trade groups to craft curriculum that’s up-to-date, and incentivize their existing workforce to learn new skills.
As a group, these challenges appear daunting. Enterprises and banks are recommended to break out each one separately, then solve one-by-one.
As more companies lean into the overall ‘lift’ required by AI, a unified approach will emerge full of industry best practices.
AI’s Influence Creates Trends in Financial Services
With AI adoption increasing across the globe, industry trends are beginning to form — raising the stakes for firms of all sizes to fully commit to artificial intelligence in the next year.
Here are three trends to follow closely:
Expansion of Financial Services
(Highlighted earlier) AI’s ability to provide insights from alternative data increases access to unbanked and underbanked individuals worldwide.
This extends the vision from Fintech that “technology in banking is able to democratize the industry for groups of various backgrounds and incomes.”
Widening AI Ecosystems
The network effects from multiple parties working together (e.g. regulators, financial institutions, tech vendors, and fintechs) will create a collaborative environment.
This new ecosystem will help solve challenges with much more specificity and speed.
Combining Emerging Technologies
AI in conjunction with blockchain, internet of things (IoT), and quantum computing can fast forward further innovation within shorter time frames.
An AI-Powered Future Awaits
As artificial intelligence continues to reshape the financial services industry, enterprises & financial institutions are scrambling to decide how best to adopt new solutions.
In calling out top challenges and identifying how to overcome them, senior leaders are able to demystify what’s best in an approach that allows their company to benefit from AI.
In 2025, there is no established path for organizations to adopt AI solutions.
What is certain is that companies able to successfully navigate the journey ahead will capture the benefits, overcome challenges, and evolve their own strategies to help them emerge as industry leaders.
Thanks for following our series finale on AI in Financial Services!
(If you haven’t already), please check out parts 1-3:
1: “How AI Redefines Financial Services in 2025.”
2: “Responsible Innovation: Ethical AI Adoption in Finance.”
