Benefits and challenges of enterprise AI in the banking and finance sector

Artificial Intelligence (AI) is something you hear everywhere now. Following the last several years, interest in technology has skyrocketed, with people searching for the term six times more than at the end of 2022 (see Fig.1). Yet, only few understand the true potential of AI and not just follow the trend.

Figure 1. AI keyword traffic volume

AI has been changing banking for more than ten years now. With increasing data volumes and evolving customer expectations, financial institutions have turned to AI to enhance efficiency, manage risks, and deliver superior customer experiences.

According to a report by Business Insider Intelligence, banks saved $447 billion in 2023 by implementing AI applications. Moreover, the global AI in the banking market is anticipated to be worth $64.03 billion by 2030, with a CAGR of 32.6% from 2021 to 2030.

The more technology develops and is in demand, the greater opportunities arrive. Now, it is the turn of the enterprise AI—AI applications designed to meet the complex needs of large organizations. Unlike ordinary AI applications, enterprise AI integrates deeply into business processes, enabling organizations to leverage data-driven insights at scale.

This article explores enterprise AI, its benefits and challenges in banking and finance. And offers insights into future outlooks on technology. Stay tuned.

What is enterprise AI, and how is it different from ordinary AI?

The concept of enterprise AI has emerged as a critical differentiator for large organizations. Unlike conventional AI applications that may operate in isolation, enterprise AI is embedded deeply within the organizational fabric, influencing decision-making processes, optimizing workflows, and driving innovation at scale.

Defining enterprise AI

Enterprise AI is the comprehensive integration of AI technologies across an organization’s entire ecosystem. It enables intelligent automation, advanced analytics, and data-driven decision-making on a large scale. Enterprises can optimize complex business processes and foster innovation by deploying AI capabilities such as Machine Learning (ML), Natural Language Processing (NLP), and Computer Vision.

For example, seven years ago, in 2017,  JPMorgan Chase developed a COiN (Contract Intelligence) program that uses machine learning (ML) to review legal documents and extract important data points and clauses. This enterprise AI application reportedly reduced 360,000 hours of legal work annually to seconds. 

Now, project how much AI has changed, and you will understand the potential scale of enterprise AI’s capabilities.

Key components of enterprise AI

When it comes to the key parts of enterprise AI, one should mention:

• Scalable infrastructure
• Advanced analytics
• Integration with legacy systems
• Compliance and governance

1. Scalable infrastructure

In simple terms, it is a robust computing power and storage solution capable of handling vast amounts of data and complex computations. Banks like HSBC invest in enterprise AI to fight off fraud. Their system checks 1.35 billion transactions monthly. Such massive data processing capabilities save a lot of time and resources.

2. Advanced analytics

It is all about using sophisticated tools and algorithms for predictive and prescriptive analytics to gain actionable insights. Why is it important? 77% of banking executives believe that getting value from customer insights is vital for competitive advantage. With predictive and prescriptive analytics, enterprise AI can offer deeper insights into customer behaviors.

For instance, Wells Fargo uses AI-based advanced analytics to deliver hyper-personalized services. It has already shown a 20% improvement in customer satisfaction and a 40% cross-selling rate increase.

3. Integration with legacy systems

One of the most important components of enterprise AI—seamless incorporation of AI solutions with existing IT infrastructure and software applications. It is crucial to stress the “seamless” part. Namely because integration with legacy systems can present some massive challenges.

However, when done correctly, integration of enterprise AI with legacy systems can bring two massive benefits:

• Increase security
• Reduce costs

McKinsey & Company reports how AI integration into legacy systems reduced costs by allowing companies to make more customer-oriented business choices.

4. Compliance and governance

This is a big one for banking and finance. Enterprise AI is about establishing frameworks to ensure data privacy, ethical AI practices, and adherence to regulatory standards. According to IBM Security, non-compliance with data regulations can cost a business an average of $14.8 million.

Difference with ordinary AI

Now, you understand what enterprise AI is and its key components. However, it is still unclear how enterprise AI differs from ordinary AI. To narrow down this gap, take a look at this table.

A simple illustration: While a small business might use a standard AI-powered email filtering tool (ordinary AI) to manage spam, a large financial institution would deploy an enterprise AI system to monitor and analyze all communications for compliance risks, integrating it with existing compliance systems.

Relevance to banking and finance

The banking and finance sector stands at the forefront of adopting enterprise AI. All due to inherently data-rich environment and stringent regulatory requirements. The industry’s reliance on vast amounts of data for operations—such as transaction processing, risk assessment, and customer service—makes it an ideal candidate for enterprise AI. According to a report by McKinsey & Company, AI technologies can deliver up to $1 trillion of extra value annually for global banking.

Financial institutions can leverage enterprise AI to enhance:

• Fraud detection
• Risk management
• Customer engagement
• Operational efficiency

Enterprise AI is exceptionally relevant to the banking and finance sector due to its capacity to handle large data volumes and assist in meeting regulatory demands. The strategic implementation of enterprise AI represents a significant opportunity for growth, innovation, and sustained success within the industry.

Enterprise AI strategy and planning

Implementing enterprise AI in the banking and finance sector complex. The process demands a strategic and well-thought-out approach. Below are critical components of an effective enterprise AI strategy, as well as insights and practical tips for each.

I. Defining clear objectives

First and foremost, establish specific, measurable objectives for AI projects that directly support the organization’s strategic goals, such as reducing fraud by a certain percentage or enhancing customer satisfaction scores.

Why is this important? 

According to Forbes, only 53% of AI projects make it from prototypes to production, often due to misaligned objectives

Pro Tip: Bring together stakeholders from different departments to discuss and prioritize AI initiatives. This ensures that everyone understands how AI projects contribute to overall business objectives.

II. Data readiness assessment

Second, the organization’s data assets must be assessed to ensure they are suitable for AI applications. This includes evaluating

• data quality;
• data completeness;
• data accessibility;
• compliance with privacy regulations.

Why is this important? 

According to IBM, poor data quality averages $12.9 million annually in costs.

Pro Tip: Conduct a thorough audit to identify data sources, evaluate data quality, and pinpoint gaps. Use data profiling tools to automate this process.

III. Building cross-functional teams

Next, form multidisciplinary teams to ensure that AI solutions are technically sound, compliant with regulations, and aligned with business needs.

Why is this important? 

According to Deloitte, cross-functional teams offer a major boost in terms of innovation and adaptability.

Pro Tip: Establish a centralized team responsible for the organization’s AI strategy, best practices, and knowledge sharing.

IV. Regulatory compliance

Navigate the complex regulatory landscape by ensuring enterprise AI complies with data protection laws, financial regulations, and ethical standards.

Why is this important? 

According to GDPR policies, non-compliance can cost up to 4% of a company’s global turnover. And fines reaching as high as 20 million euros.

Pro Tip: From the outset, legal and compliance teams should be involved in identifying regulatory requirements and incorporating them into AI system design.

V. Scalability planning

Further, make sure that AI solutions can handle increasing data volumes, user loads, and computational demands over time. Use modular architectures and microservices to enable easy updates and integration of new functionalities.

Why is this important? 

According to the International Journal of Information Management, businesses designing AI for scalability are better adapted to market changes and can meet customer needs more precisely.

Pro Tip: Utilize cloud services for scalable computing resources. This allows for flexibility and cost-effective scaling as demands change.

VI. Change management

Finally, you need to address the human aspect of AI implementation by managing transitions in roles, processes, and organizational culture. Still, human error is a major hurdle in digital transformation and technological adaptation.

Why is this important? 

According to McKinsey & Company, companies with effective change management are more likely to outperform competition.

Pro Tip: Include clear communication strategies, training programs, and feedback mechanisms to support employees through the transition.

Implementing enterprise AI strategically is crucial to unlock its full potential in banking and finance. Organizations must carefully plan each aspect to navigate the complexities of AI adoption. This includes setting clear objectives and ensuring data readiness. Fostering collaboration, ensuring compliance, preparing for scalability, and managing change are essential.

Such an approach mitigates risks. It positions financial institutions to leverage AI for competitive advantage, innovation, and sustained success in a rapidly evolving industry.

How businesses use enterprise AI in banking and finance

Financial institutions are leveraging AI to tackle complex challenges and seize new opportunities. Below are key areas where enterprise AI is making a significant impact, supported by real-life examples and industry insights.

• Fraud detection
• Risk management
• Customer service
• Automated trading
• Personalized finance

1. Fraud detection

Financial fraud costs the industry billions annually, which is one reason the global cybersecurity market is booming (see Fig. 2). Enterprise AI enhances fraud detection by analyzing real-time transaction patterns to identify anomalies indicating fraudulent activity.

Figure 2. Global cybersecurity market size

As per the Association of Certified Fraud Examiners (ACFE), estimated 5% of annual revenue is lost due to fraud. AI-driven fraud detection systems can reduce detection time by up to 50%, enabling quicker responses and prevention.

How does that work? As per the Alan Turing Institute, AI models process vast amounts of raw data faster than human analysts. That is why enterprise AI can catch even the slightest instances of fraud.

Example: PayPal uses enterprise AI and ML algorithms to detect fraudulent transactions among its 286 million active user accounts. The AI system analyzes transaction history, location data, and device information to flag suspicious activities. This has helped reduce PayPal’s fraud rate to 0.32%, which is significantly lower than the industry average.

2. Risk management

Enterprise AI assists in assessing credit risks and predicting market trends, enabling institutions to make informed decisions and mitigate potential losses. 

How does that work? AI algorithms analyze unstructured raw data, such as news articles and social media sentiment, and provide a more comprehensive view of risk factors affecting investments or creditworthiness.

Example: BlackRock, the world’s largest asset manager with$15 trillion in management, uses its enterprise AI-driven platform, Aladdin, to assess risk across its portfolios. Aladdin analyzes over 30,000 investment portfolios daily, evaluating market trends, credit risks, and potential impacts on assets.

3. Customer service

The future of conversational AI is bright. There is a growing demand for chatbots out there (see Fig. 3). What is more, according to Juniper Research, chatbots in banking will reach 862 million users globally by the end of 2024, saving banks an estimated $7.3 billion in operational costs.

Figure 3. Chatbot market size

How does that work? Chatbots and virtual assistants provide instant responses to inquiries, handle routine tasks, and free human agents up for more complex issues. They also reduce response times and handle many queries simultaneously, enhancing customer satisfaction and loyalty.

Example: Swedbank implemented an AI chatbot named Nina to handle customer queries. Nina successfully managed over 40,000 conversations per month, resolving 81% of customer inquiries without human intervention.

4. Automated trading

Enterprise AI algorithms execute trades based on market data analysis, news sentiment, and historical patterns. As a result, companies, banks, and financial institutions can react to market changes faster. Faster reactions equals less money lost and more money made.

How does that work? Automated trading systems reduce human error and emotional bias, leading to more consistent trading strategies and potentially higher returns. Loeb & Loeb discuss how AI-driven trading systems can process and react to data in milliseconds.

Example: Citadel LLC, a leading hedge fund, uses AI-driven automated trading systems to execute highly efficient trades. Their algorithms analyze market data in real-time, making split-second decisions that contribute to the firm’s success in high-frequency trading.

5. Personalized finance

AI enables personalized financial services by analyzing customer data to offer tailored product recommendations, financial advice, and customized experiences. According to Accenture, 78% of consumers are more likely to engage with offers if they are personalized based on previous interactions.

How does that work? As per SAP, enterprise AI can process vast amounts of customer information to deliver customized experiences, increasing customer loyalty and retention.

Example: Mint, a personal finance app, uses AI algorithms to give users personalized budgeting advice and financial insights. The system works based on users’ spending habits and financial goals. This customized approach has attracted over 20 million users.

Enterprise AI is transforming the banking and finance sector. It enhances fraud detection, improves risk management, elevates customer service, enables automated trading, and offers personalized financial services.

Naturally, their integration into banking and finance will deepen as AI technologies evolve. As a result, this will drive innovation and shape the industry’s future.

Five key benefits of enterprise AI

The list of potential enterprise AI benefits is long. Yet, not many potential advantages have been proven to bring tangible positive results in practice. That is why, in our list of benefits, the major focus was on the advantages already proven to work.

I. Greater operational efficiency via routine task automation

Enterprise AI significantly enhances operational efficiency by automating routine tasks. The statistics show that almost half of employees spend 25% of their work time on manual repetitive tasks. AI reduces manual errors and operational costs by handling repetitive and time-consuming activities. 

According to the McKinsey Global Institute, AI could boost productivity in banking, primarily through automation. AI automation frees up employees to focus on strategic initiatives like innovation and customer engagement. As a result, with more automated workflows, organizations and businesses operate more efficiently.

II. Improved decision-making with on-point predictive analytics

The global predictive AI market is getting more and more traction (see Fig. 4). This means more companies use it, which urges their competitors to want it as well. Such an approach creates a loop driving the entire market. However, when used correctly, AI-based predictive analytics provide actionable insights, allowing financial institutions to forecast market trends, assess risks, and make data-driven decisions. 

Figure 4. The global predictive AI market

Returning to the HSBC example. The financial institution utilizes AI-powered analytics to detect money laundering and financial crimes. The system analyzes transaction patterns and anomalies. And this helps compliance teams make informed decisions about potential risks.

A Deloitte survey found that 70% of financial services firms use AI for risk management, with AI models improving predictive accuracy by up to 20% compared to traditional methods.

III. Better customer experience through personalization and support

This article has already partially covered the personalization-based aspect of enterprise AI. While the technology has proven to improve customer satisfaction,  personalization also increases customer engagement. It is all based on a deeper understanding of customer needs.

I’ve long believed that AI won’t just enhance the way we live, but transform it fundamentally. … AI is placing tools of unprecedented power, flexibility, and even personalization into everyone’s hands, requiring little more than natural language to operate. They’ll assist us in many parts of our lives, taking on the role of superpowered collaborators.

Silvio Savarese,
Executive Vice President and Chief Scientist, Salesforce AI Research

According to Salesforce, 62%of customers expect businesses to adapt to customer behaviors as well as demands. This underscores the demand for personalized services. Yet, even without statistics, it is clear people interact with AI at a far greater scale, take ChatGPT or Midjourney for an example. Naturally, such interaction translates into B2B, B2C, and B2G realms.

AI fosters stronger customer relationships by delivering personalized experiences and immediate support. As a result, one can expect greater customer loyalty and retention.

IV. Higher competitive advantage via differentiation

Perhaps the term “competitive advantage” is almost as widely used as “Artificial Intelligence.” While particular cliches are attached, no one can deny the importance of competitive advantage. The very framework of a capitalistic economy is based on competition. As a result, everything that can differentiate a business or an organization in a highly competitive market is worth considering if such a practice is ethical and regulated, of course.

How does it work with enterprise AI? Early adopters of AI can offer innovative products and services that attract and retain new customers, staying ahead of industry trends.

For example, Revolut, a fintech company, uses AI to provide instant spending notifications, budgeting tools, and fraud detection, attracting over 15 million users globally. A report by Accenture indicates that banks leveraging AI technologies could increase their profitability by an average of 38% by 2025. Higher profits directly translate into a competitive advantage.

Enterprise AI facilitates rapid innovation. Respectively, institutions introduce new services that meet evolving customer expectations and outperform competitors.

V. More precise regulatory compliance support with automated monitoring

Enterprise AI supports regulatory compliance by automating monitoring and analysis. AI systems can continuously analyze transactions to flag potential violations. As a result, reducing the risk of fines and reputational damage. 

For instance, Danske Bank implemented an AI-based anti-money laundering (AML) system that improved the detection of suspicious transactions by 50% while reducing false positives.

Enterprise AI systems learn and adapt to new regulatory requirements. This proactive approach not only safeguards against legal repercussions but also reinforces the institution’s commitment to ethical standards and regulatory adherence.

The benefits of enterprise AI in banking and finance are substantial. By embracing enterprise AI, financial institutions can transform their operations, meet evolving customer demands, and navigate the complexities of the modern economic landscape with greater agility and confidence.

Five main challenges of enterprise AI

While enterprise AI offers transformative potential for the banking and finance sector, its implementation is not without significant challenges.

Let’s look at these hurdles and explore the recommendations designed to address them.

I. Data security and privacy

Banking and finance are the sectors most affected by data breaches (see Fig. 5). It is only logical that fraudulent activities are directed at places where money is. In such a context, banks and financial institutions cannot afford taking data security and privacy for granted.

Figure 5. Percentage of data breaches by industry

As mentioned above, enterprise AI works with vast amounts of data. It uses raw unprocessed data to get market insights. However, for personalization purposes, enterprise AI works with customer data. This is the moment where everything becomes tricky. When tuned improperly, enterprise AI can result in data breaches. As an outcome, organizations get litigation and massive reputation damages.

Recommendations:

• Implement advanced security measures. Utilize encryption, multi-factor authentication, and intrusion detection systems to protect data.
• Regular security audits. Conduct quarterly assessments to identify vulnerabilities and ensure compliance with security standards.
• Employee training. Educate staff on data handling and cybersecurity best practices to minimize human error.
• Adopt security frameworks. Align with standards like ISO 27001 or NIST to systematically manage sensitive information.

II. Regulatory hurdles

Compliance with regulations like GDPR, DORA, and industry-specific laws can complicate AI deployment. Financial institutions must ensure AI models are transparent and explainable to meet regulatory standards. 

Recommendations:

• Stay informed on regulations. Maintain a dedicated compliance team to monitor and interpret regulatory changes.
• Implement explainable AI models. Use AI systems that provide transparency in decision-making processes.
• Collaborate with legal experts. Work closely with legal advisors to make certain AI initiatives meet regulatory requirements.
• Regular compliance audits. Periodically review AI systems to ensure ongoing adherence to laws and regulations.

III. Resource allocation

Implementing enterprise AI requires significant investment in technology infrastructure and skilled personnel. Budget constraints limit the scope and effectiveness of AI projects.

Recommendations:

• Phased implementation. Roll out AI projects in stages to manage costs effectively.
• Cloud services. Use cloud-based AI platforms to reduce the need for expensive on-premises infrastructure.
• Return on Investment (ROI) analysis. Conduct thorough cost-benefit analyses to prioritize projects with the highest return on investment.
• Seek partnerships and grants. Explore collaborations or funding opportunities to offset costs.

IV. Talent shortage

Expertise is needed to develop and maintain enterprise AI systems. Currently, there is a massive talent shortage among professionals who work with AI (see Fig. 6). Talent competition can drive up costs and delay project timelines.

Figure 6. Amazon, Apple, Google, Meta, and Microsoft monthly employment of AI-related staff

Recommendations:

• Invest in employee development. Offer training programs to upskill existing staff in AI technologies.
• Recruit globally. Expand hiring efforts beyond local markets to tap into a wider talent pool.
• Partner with educational institutions. Collaborate with universities for internships and research projects to cultivate future talent.
• Use off-the-shelf AI platforms. Adopt user-friendly AI tools that require less specialized expertise.

V. Ethical considerations

Finally, there are some ethical concerns related to enterprise AI and AI in general. There is a real concern that with the increase of synthetic AI-generated data coursing through the Internet, soon, there will be no evidence-based data left. What is more, AI systems can perpetuate biases in training data, leading to unfair outcomes. Entering such grey areas can hurt the outcomes you want to achieve.

Recommendations:

• Establish ethical guidelines. Develop a clear set of principles governing AI development and use within the organization.
• Bias detection and mitigation. Implement tools and processes to identify and reduce bias in AI models.
• Promote transparency. Ensure AI systems are explainable, allowing stakeholders to understand how decisions are made.
• Engage stakeholders. Involve a diverse group of employees and external advisors in reviewing AI initiatives for ethical considerations.
• Regular ethical audits. Periodically assess AI systems to ensure they align with ethical standards and societal values.

Success in creating effective AI, could be the biggest event in the history of our civilization. Or the worst. We just don’t know. So, we cannot know if we will be infinitely helped by AI, or ignored by it and side-lined, or conceivably destroyed by it.

Stephen Hawking

Addressing the aforementioned challenges proactively is essential for unlocking the full benefits of enterprise AI in banking and finance. Such a comprehensive approach mitigates risks and positions financial institutions to leverage AI for innovation, competitive advantage, and sustained success in a rapidly evolving industry.

Conclusion

What do we know at the moment?

1. Enterprise AI is more sophisticated and complex than ordinary AI.
2. Banks and financial institutions already use it to automate tasks, detect fraud, mitigate risk, and improve personalization.
3. While there are many more benefits to appear in the near future, privacy, security, and ethics are still key concerns with enterprise AI.

Where do we go from here?

1. More transparent and understandable enterprise AI.
2. Less Fear of missing out (FOMO) and a more evidence-based approach.

It is clear that enterprise AI changes and will further change banking and finance. Currently, there are many more benefits than challenges involved. With right recommendations and advice, many of the hurdles can be mitigated.

Ready to harness the power of enterprise AI in your financial institution? Meet Pawel and Ludovic Gaude, Avenga’s and Qinshift’s CEO, at GITEX 2024 on October 15-16.

Book your meeting now to discover innovative AI solutions, and take the first step toward accelerating your AI transformation.