Staying competitive in 2025 as a Financial Services Org amidst AI & Low-Code

By 2025, AI and low-code platforms are essential for financial institutions to reduce costs, improve efficiency, and combat rising cybercrime (expected to cost $10.5 trillion annually). Here’s what you need to know:

• AI Benefits: Detects fraud (nearly 50% of fraud cases), predicts risks, and personalizes services.

• Low-Code Impact: Cuts app development time by 62%, adds $14.8M in annual revenue, and simplifies customer service.

• Key Trends:

  • 87% of customers prefer self-service options.

  • Embedded finance is growing, with 72% of IT leaders expecting financial products on non-financial platforms.

  • Cybersecurity tools powered by AI are critical to combating threats.

• Results: NatWest reduced product governance time from 4.5 days to 20 minutes using automation, while Western Union launched digital banking in two countries in 11 months with low-code.

To stay ahead, financial organizations must adopt these tools, focus on data security, and balance innovation with compliance.

The Rise of AI: Fintech Predictions for 2025

AI and Low-Code Impact on Financial Services

The financial services industry is undergoing a major transformation as AI and low-code technologies change how banks and other institutions operate. These advancements are reshaping everything from customer service to risk management and compliance processes.

Market Trends Driving Technology Adoption

Several market trends are pushing financial institutions toward AI and low-code solutions. For one, customer expectations have shifted significantly. A survey shows that 87% of U.S. customers prefer self-service options, and 66% favor automated services over speaking with a representative. This change has forced financial institutions to rethink how they deliver services.

Another major trend is the rise of embedded finance. 72% of finance IT leaders anticipate that most financial products will soon be offered through non-financial platforms. This requires faster, more flexible development capabilities that traditional coding methods struggle to provide. Additionally, growing cybersecurity concerns are driving the adoption of AI-powered security tools across the sector.

"Resilience will be key to the future of financial services. Because this push for a culture of innovation will not come without its challenges. As more jurisdictions follow the EU's lead in introducing regulations, financial institutions will find themselves balancing innovation with the critical need to ensure security, compliance, and customer satisfaction." - Appian

These trends not only alter market strategies but also deliver measurable results for organizations that adopt these technologies early.

Benefits of Early Technology Implementation

Early adopters of AI and low-code solutions are reporting impressive outcomes:

A great example is Western Union, which launched new digital banking services in two countries within just 11 months. They achieved this by leveraging low-code tools for customer experience and workflow automation.

The success of AI implementation in financial services often depends on four key applications:

• Predictive AI for market analysis and risk assessment

• Anomaly detection AI for fraud prevention

• Classification AI for customer segmentation

• Generative AI for personalized service delivery

As financial institutions adopt these technologies, they must prepare for stricter regulatory oversight. Establishing strong governance frameworks now will help organizations stay compliant while continuing to innovate. These tangible benefits highlight why integrating AI and low-code solutions is becoming a priority for financial institutions.

AI Tools for Financial Analysis and Risk

AI is playing a key role in helping financial institutions stay ahead in 2025. With fraud losses reaching $8.8 billion in 2022, many organizations are turning to AI to improve risk assessment and fraud prevention. Below, we’ll explore how AI enhances risk detection, supports product development, and provides guidelines for effective adoption.

Risk Detection and Fraud Prevention

AI can monitor transactions, analyze patterns, and identify irregularities in real time. This helps protect both consumers and financial institutions by quickly spotting potential threats.

"AI algorithms can continuously monitor transactions and flag those that deviate from norms or match fraud patterns. Banking and payments fraud losses have decreased due to this scrutiny, ensuring consumer and financial institution security." - Dimple Patil, Hurix Digital

Here are some key areas where AI improves detection:

In addition to fraud detection, AI is also shaping the development of new financial products.

AI-Based Financial Product Development

Generative AI tools have the potential to save the global financial services industry between $200 billion and $340 billion annually. For example, Capital One filed 55 AI-related patents in Q3 2022 and 47 in Q2 2022, showcasing its commitment to AI-driven innovation.

Some AI platforms improving financial services include:

• Zest AI for assessing lending risks

• AlphaSense for tracking market trends

• Spindle AI for sales predictions

• Quantivate for managing risks

AI Implementation Guidelines

To make the most of AI, organizations should focus on three key areas:

1. Data Security and Privacy: Use private AI models to ensure sensitive data remains protected.

2. System Integration: Implement AI tools that integrate seamlessly with existing workflows. For instance, Arya.ai's Apex platform offers APIs for analytics and forecasting.

3. Staff Training: Equip teams with the skills to use AI tools effectively and establish clear decision-making protocols.

"The primary benefit of AI for financial services is the impressive computational speed and the analytical potential it offers, allowing quicker and more sensitive decisions based on accurate analytical prognoses." - 4IRE

With AI offering substantial savings and operational improvements, adopting these guidelines can help financial institutions maintain a competitive edge in an ever-changing industry.

Low-Code Solutions for Customer Service

Low-code platforms are reshaping customer service by improving efficiency, reducing costs, and speeding up product launches. These tools allow banks to deliver smooth, personalized experiences across various channels.

Multi-Channel Service Development

Low-code technology has revolutionized how banks create and roll out customer-facing services. Using visual tools, financial institutions can now develop integrated banking solutions for web and mobile platforms up to 90% faster.

Here’s how banks are benefiting from multi-channel development:

These time and cost savings open up opportunities for banks to deliver more tailored, customer-centric solutions.

Customer Experience Customization

Low-code platforms give banks the tools to stand out by offering unique customer experiences. This is crucial, as over one-third of U.S. bank customers feel that "all banks are the same".

"Mendix makes development a visual process with high levels of abstraction and automation. Development becomes a collaborative, iterative project with fast results and fantastic customer satisfaction."

Some of the key features driving customization include:

• Pre-built templates for processes like onboarding and loan applications

• Customizable interfaces for different customer groups

• Real-time updates to adapt to changing customer needs

• Seamless integration with older systems to ensure smooth data flow

Success Story: Low-Code Customer Service

Real-world examples show just how effective low-code solutions can be. Rabobank’s RaboDirect portal is a standout case. This internet banking platform now manages over €18 billion for 500,000 customers.

"We were able to build functionality and release it in a fast cycle…to do it with a relatively small team with mostly - and this is important to me - business-oriented developers rather than code-oriented developers. This would enable us to get really good cooperation with the business people, the product owner, and the end user. That's why we liked that idea so much." – Joost Landman, IT Architect at Rabobank

Rabobank’s approach led to a 50% reduction in IT costs, streamlined operations across seven countries, and faster feature rollouts. Similarly, ABN AMRO showcased the speed of low-code by building a mobile version of their Pre Trade Counterparty Manager tool in just four hours during a hackathon.

Improving Operations with Technology

Technology isn't just enhancing customer service and risk management; it's also transforming internal operations. By combining AI with low-code solutions, financial institutions are cutting costs and improving service quality across the board.

Back-Office Process Automation

AI-driven automation is reshaping back-office processes, significantly lowering costs while improving efficiency. According to McKinsey's 2023 report, generative AI tools could save the global financial services industry between $200 billion and $340 billion annually.

Here's how automation is making an impact at major financial institutions:

Faster App Development Methods

Low-code platforms are revolutionizing app development, boosting productivity by 123%. These platforms allow teams to focus on innovative projects instead of repetitive coding tasks. To make the most of these tools, financial institutions should emphasize:

• Ensuring high-quality data

• Implementing thorough testing protocols

• Strengthening encryption standards

• Adhering to regulations like PSD2 and GDPR

Compliance and Report Automation

AI is also transforming compliance and reporting tasks, which have become increasingly costly. Since 2008, compliance costs for banks have risen by 60%, with 6–10% of their revenues now allocated to these activities.

Leading banks are showcasing AI's potential in compliance:

• HSBC uses AI to monitor credit card fraud.

• Standard Chartered employs AI for anti-money laundering efforts.

• UBS leverages AI chatbots to assist compliance officers with regulatory queries.

Generative AI is further reducing compliance time and expenses:

"Humans are the ultimate decision-makers and will continue to be for the foreseeable future. But generative AI can augment human capabilities by being less error-prone, more productive and more efficient, as well as by addressing the most tedious and time-consuming tasks more predictably." - Saket Sinha, Senior Partner and Vice President Financial Services, IBM Consulting

Implementation Guide for AI and Low-Code

Building on the earlier discussion of AI and low-code tools, this guide provides practical steps to help organizations implement these technologies effectively.

Technology Readiness Check

Before diving into AI and low-code solutions, it's crucial to evaluate your organization's technological readiness. Gartner suggests using a structured approach to assess key areas.

This evaluation helps identify gaps and prioritize investments. Notably, 44% of industry leaders already support digital transformation efforts.

Team Structure and Collaboration

A successful implementation depends on assembling a well-rounded team that blends technical knowledge with business insights. Key roles include:

• AI Council: Senior executives who set strategic goals.

• Implementation Team: Developers and system architects.

• Business Process Experts: Specialists who understand workflows.

• Change Champions: Department representatives who advocate for adoption.

"Organizations are beginning to see AI not as a panacea but as a powerful, albeit complex, tool that requires thoughtful implementation."
– Alex Ford, Global Chief Revenue Officer and President of North America, Encompass

Once the team is in place, focus on managing organizational change to ensure smooth adoption.

Change Management Steps

Managing change effectively is key to turning plans into lasting outcomes. In fact, 25% of banking executives identify this as a major challenge. Here's a practical framework to guide the process:

1. Initial Assessment and Planning
   

   Begin with a stakeholder analysis and set clear timelines for both short- and long-term goals.

2. Skills Development
   

   CEOs predict that 35% of the workforce will need retraining over the next three years. Training should cover areas such as:

   Training Area	Purpose	Expected Outcome
   AI Literacy	Understanding AI capabilities	Better decision-making
   Low-Code Development	Hands-on experience with platforms	Faster app development
   Data Analytics	Interpreting AI-generated insights	Improved business strategies

3. Implementation and Monitoring
   

   Set up feedback loops to refine processes and ensure the technology meets business objectives. Regularly track metrics to measure success.

"A one-size-fits-all approach will not work for every company. Each finance organization will require a tailored AI strategy that reflects an enterprise's unique circumstances."
– Gartner

For example, banks can use AI for fraud detection and regulatory compliance, ensuring human oversight remains intact. Regular progress reviews will help keep efforts aligned with both company goals and industry standards.

Conclusion

By 2025, staying competitive in financial services will hinge on combining AI with low-code tools. Banks using low-code platforms have already seen faster development and higher revenue growth. This shift is no longer optional - it's a necessity in today's digital economy.

AI and process automation could bring in $200-340 billion annually in productivity gains for the banking sector. At the same time, institutions must contend with escalating cybercrime costs, which are expected to hit $10.5 trillion by 2025.

"In 2025, AI use in financial services won't be a differentiator. It will be a requirement for survival in a landscape that it has already irreversibly altered." - Anand Pandya

Examples from NatWest and Western Union highlight how automation and agile digital services can deliver real results. These examples provide a roadmap for what financial institutions should prioritize next.

Key Areas of Focus for Financial Institutions

To navigate this rapidly evolving landscape, financial organizations should concentrate on three main areas:

• Data Management: Establish strong data governance to ensure AI models are interpretable, unbiased, and reliable.

• Security and Compliance: Strengthen security frameworks while adhering to regulations, especially as AI-driven personalization raises privacy concerns.

• Strategic Rollouts: Start with pilot programs to build internal expertise and prove measurable results before scaling up.

Balancing innovation with risk management is essential. As Jamie Dimon, CEO of JPMorgan Chase, puts it:

"The future belongs to those who can rise above the technology and master it".

Ultimately, the ability to master these challenges will define which financial institutions lead the industry in 2025 and beyond.

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• Using AI and Low-Code to Deliver Personalized Financial Services