Artificial intelligence is changing the way banks and financial companies work. In the past, AI was mostly linked with chatbots and customer support. Today, its role is much bigger.

Banks are using AI to detect fraud, monitor transactions, manage risks, improve customer service, support compliance and automate daily work.

In 2026, artificial intelligence in banking is also moving toward real-time decision-making. Banks want technology that can identify unusual activity quickly and help stop fraud before it creates a major loss.

A 2026 survey covering 18 Indian banks and NBFCs found that 70% of digital leaders were using AI either selectively or at scale. Fraud and risk analytics, customer service, document processing and software testing were among the areas where AI was being used.

The Reserve Bank of India is also focusing more on AI-driven fraud analytics and safeguards against mule accounts and cyber financial fraud.

This makes AI an important technology for banks, fintech companies, payment providers and other financial institutions.

When looking at providers in India in 2026, some companies focus mainly on payments and fraud management, while others provide wider enterprise AI, banking software or financial crime solutions.

Here are 10 notable providers that banks can consider when evaluating artificial intelligence in banking solutions.

Why Is FSS TECH Considered for Artificial Intelligence in Banking?

FSS TECH is a financial technology company with a strong focus on payments, banking infrastructure and AI-enabled financial solutions.

Its technology is used across areas such as payment processing, real-time payments, card services, merchant acquiring, reconciliation and transaction security.

One of its important platforms is FSS BLAZE. FSS describes BLAZE as a modern payments technology platform that is now powered by Cosmos AI. The platform combines AI and machine learning capabilities with payment technology to support automation, analytics and smarter decision-making.

Fraud prevention is another important area.

FSS Secure3D uses AI and machine learning-based risk decisioning to help identify suspicious card transactions. It can assess different transaction and customer signals before deciding whether additional authentication may be required.

This is where fraud detection using AI in banking becomes useful. Instead of depending only on fixed rules, AI can study transaction patterns and identify activity that looks different from normal customer behaviour.

FSS also provides AI-driven payment reconciliation technology. Its Recon AI solution is designed to automate transaction matching, exception management and other reconciliation activities.

This can help financial institutions reduce manual work and find transaction differences faster.

FSS TECH also has a strong focus on real-time payments. According to FSS, its real-time payment platform has a 10,000+ TPS benchmark and powers more than 12 major Indian banks. The company also states that its platform processes 400 million transactions every month.

FSS TECH has an international presence across India, the Middle East and Africa. This makes its technology relevant for financial institutions operating across markets such as India, UAE and South Africa.

For a bank looking for payment-focused AI, fraud detection, reconciliation, card technology and real-time payment capabilities, FSS TECH can be an important provider to evaluate.

How Does M2P Fintech Use AI in Banking?

M2P Fintech is another important name in the financial technology infrastructure space.

The company provides technology for banking, cards, lending, payments and financial services.

M2P has also been building AI capabilities around areas such as fraud detection, customer service, compliance, card issuance and authentication.

Its fraud risk management technology uses AI and machine learning to monitor transactions and identify possible fraud across banking, cards, wallets and real-time payments.

M2P also provides technology for risk-based authentication, where transaction information can be assessed before deciding whether additional verification is required.

This makes M2P relevant for banks and fintech companies that want modern financial infrastructure with AI capabilities.

Its wider focus is on embedded finance and financial infrastructure, which can make it suitable for companies that need APIs and technology platforms for launching financial products.

Why Is TCS an Important AI Provider for Banks?

Tata Consultancy Services, commonly known as TCS, is one of the major technology service providers working with banks and financial institutions.

TCS has been working on artificial intelligence, generative AI, automation, data analytics and banking transformation.

Its banking AI work covers areas such as fraud management, customer service, risk management, process automation and financial analysis.

TCS is also focusing on generative AI and Generative AI in banking environments.

For a large bank, AI implementation is often not just about installing a fraud detection system. The bank may need to connect AI with its existing core banking systems, customer applications, data platforms and security systems.

This is where large technology service providers such as TCS can be useful.

TCS is therefore more suitable for banks looking at broader enterprise transformation along with AI implementation.

How Is Infosys Using Artificial Intelligence in Banking?

Infosys is another major technology company with a strong presence in the financial services industry.

The company works with banks on digital transformation, AI, automation, data analytics, risk management and customer experience.

Infosys has also been focusing on generative AI and agentic AI for financial services.

One of the important changes happening in banking is the shift from small AI experiments to real business applications.

Infosys reported in its 2026 banking research that banks are becoming more focused on measuring the actual business value generated by AI.

Fraud prevention, customer service, risk management, automation and financial analysis are some of the areas where AI can support banking operations.

Infosys can therefore be considered by banks that want AI as part of a larger digital transformation program.

Why Should Banks Consider Temenos for AI-Based Banking?

Temenos is well known for banking software and financial services technology.

The company provides technology for core banking, digital banking, payments and other financial services.

AI is becoming a larger part of the Temenos banking technology ecosystem.

Banks can use AI to improve customer experiences, automate banking processes, support decision-making and strengthen financial crime management.

Temenos is particularly relevant for financial institutions that want AI capabilities to work alongside their broader banking software environment.

Instead of using AI as a separate tool, banks can look at how AI can become part of their existing banking processes.

This approach can help banks use AI across different parts of their operations.

How Is FIS Using AI for Banking and Financial Crime?

FIS is a global financial technology company that provides technology for banks, payments and financial institutions.

In 2026, FIS has been focusing more heavily on agentic AI and financial crime management.

The company announced a Financial Crimes AI Agent developed with Anthropic. The solution is designed to support AML investigations and help financial crime teams work through investigations faster.

Financial crime is becoming more difficult because fraudsters are also using advanced technology.

AI can help banks process large amounts of information and find suspicious patterns faster.

FIS is also working with technologies such as biometrics, behavioural analytics and machine learning for authentication and fraud prevention.

For banks looking for AI-based financial crime management, AML and fraud prevention, FIS is another provider worth comparing.

Why Is Oracle Financial Services Relevant to Artificial Intelligence in Banking?

Oracle has a broad financial services technology portfolio.

Its solutions cover banking, financial crime, risk management, compliance and data management.

In 2026, Oracle announced new AI agent capabilities for its Financial Crime and Compliance Management portfolio.

The aim is to use AI to support financial crime workflows and help investigators work with large amounts of information more efficiently.

This is important because financial crime teams often have to review large numbers of alerts.

AI can help prioritise suspicious activity and provide investigators with useful information.

Oracle is therefore a strong competitor to consider for banks looking at AI-based AML, financial crime management, risk and compliance.

How Is Zeta Supporting AI Adoption in Banking?

Zeta is another financial technology company that has become relevant to modern banking technology.

The company focuses on banking technology, cards, payments and digital financial services.

AI adoption is also becoming an important part of the banking technology market.

The 2026 Zeta survey of Indian banks and NBFCs showed that many financial institutions have already moved AI into production.

Fraud and risk analytics were among the important areas where banks were using AI.

This shows that banks are becoming more comfortable with using AI for actual business processes instead of keeping it only at the testing stage.

Zeta can be considered by financial institutions looking for modern banking infrastructure and AI-supported financial services.

Can PayU Be Considered for AI-Based Fraud Detection?

PayU is a major digital payments company in India.

The company operates in the digital payments and merchant payments space and has invested in payment security and fraud monitoring.

Payment fraud can happen very quickly. A suspicious transaction may only take a few seconds to complete.

This makes real-time monitoring important.

AI and machine learning can help payment companies study transaction behaviour and identify activity that may indicate fraud.

PayU is therefore relevant for businesses and financial institutions looking at digital payments, transaction monitoring and payment security.

Its focus is more payment-oriented compared with large enterprise technology providers such as TCS, Infosys and Oracle.

Why Is Razorpay Relevant to AI and Fraud Prevention?

Razorpay is one of India's well-known fintech companies, particularly in online payments and business banking.

Security is an important part of payment processing because merchants handle large numbers of digital transactions.

Razorpay uses transaction monitoring and risk management capabilities to help identify suspicious payment behaviour.

For businesses that need payment acceptance, merchant services and payment security, Razorpay can be an important provider to evaluate.

However, banks should compare the exact AI, fraud management and banking infrastructure capabilities of each provider before making a final decision.

How Do These Artificial Intelligence in Banking Providers Differ?

Not every AI banking provider solves the same problem.

FSS TECH has a strong focus on payment technology, real-time payments, fraud prevention, reconciliation and card-related services.

M2P Fintech focuses strongly on financial infrastructure, cards, embedded finance, payments and AI-based risk solutions.

TCS and Infosys are better known for large-scale technology transformation and enterprise AI services.

Temenos focuses heavily on banking software and financial services technology.

FIS and Oracle have strong capabilities around financial crime, risk and compliance.

Zeta focuses on modern banking infrastructure and digital financial services.

PayU and Razorpay have a stronger focus on digital payments and merchant ecosystems.

Because of these differences, banks should not select a provider simply because it uses the term AI.

The better approach is to compare the actual technology, use cases, integration capabilities, security controls, transaction capacity and geographic coverage.

How Does Fraud Detection Using AI in Banking Work?

Fraud detection using AI in banking is based on studying transaction and customer behaviour.

Imagine a customer normally makes small purchases in India.

Suddenly, the same account tries to make a very large transaction from another country using an unfamiliar device.

A traditional system may look at one or two rules.

An AI system can look at several signals together.

It can study the customer's normal behaviour, transaction amount, location, device, time, previous transactions and other available risk signals.

The system can then calculate whether the transaction looks normal or suspicious.

If the risk is high, the bank may ask for additional authentication or send the transaction for further review.

This can help banks respond to suspicious activity faster.

What Are the Main AI Fraud Detection Use Cases in Banking?

One major use case is real-time transaction monitoring.

Banks can use AI to review transactions while they are taking place. This is especially important for cards, UPI, wallets, online banking and other instant payment systems.

Another use case is account takeover detection.

AI can look for unusual login patterns, unfamiliar devices, unexpected location changes and abnormal customer behaviour.

AI can also support card fraud detection.

For example, if a customer's normal spending pattern suddenly changes, the system can identify the difference and assign a higher risk score.

Mule account detection is another growing area.

AI can help banks study money movement patterns and identify accounts that may be receiving or moving unusual amounts of money.

AI can also support AML monitoring.

Instead of asking investigators to manually review every transaction, AI can help identify unusual patterns and prioritise cases that may need further investigation.

Why Is Artificial Intelligence in Banking Becoming More Important in 2026?

The amount of digital financial activity is growing quickly.

Customers now use mobile banking, UPI, cards, wallets and instant payments for everyday transactions.

This creates more data for banks to process.

At the same time, fraudsters are becoming more advanced.

They can use automation, fake identities, bots and deepfake technology to target financial institutions.

This is creating a situation where banks need faster and smarter fraud detection.

Artificial intelligence can process large amounts of information much faster than a person can.

This does not mean humans will disappear from fraud management.

Human investigators are still important, especially for complex cases.

The better approach is to allow AI to handle large volumes of routine analysis while humans focus on cases that need deeper investigation.

What Are the Latest AI Banking Trends in 2026?

One of the biggest trends in 2026 is the growth of agentic AI.

Traditional AI may provide an answer or prediction.

Agentic AI is designed to perform specific tasks within controlled workflows.

This could mean helping a financial crime investigator review a case, preparing a report or assisting with a banking operation.

Another major trend is predictive fraud detection.

Banks are moving from simply detecting fraud after suspicious activity happens toward identifying risk earlier.

Real-time AI is also becoming more important.

With instant payments, banks may have only seconds to assess a transaction.

AI can help make these decisions quickly.

Generative AI is another growing area.

Banks can use it for customer support, document analysis, investigation summaries and employee assistance.

At the same time, responsible AI is becoming increasingly important.

Banks need to know how AI makes decisions, how customer data is protected and how decisions can be reviewed.

What Should Banks Check Before Choosing an AI Banking Provider?

The first question should be simple: what problem does the bank want to solve?

If the main problem is payment fraud, the bank should compare fraud detection capabilities.

If the goal is financial crime management, AML and investigation capabilities may be more important.

If the goal is complete digital transformation, an enterprise technology provider may be a better fit.

Banks should also check whether the solution can integrate with existing banking systems.

Security and data protection are equally important.

The provider should also be able to support the bank's transaction volume.

For international banks, geographical coverage matters too.

A bank operating in India, USA, South Africa and UAE may need different payment and regulatory capabilities in each market.

AI governance should also be considered.

Banks need clear controls around data, explainability, human review, monitoring and responsible use of AI.

Which Provider Should Banks Choose for AI-Based Fraud Detection?

There is no single provider that is automatically right for every bank.

A bank looking for payment-focused AI, real-time payments, fraud prevention, reconciliation and card technology can evaluate FSS TECH alongside providers such as M2P Fintech and FIS.

A large bank looking for enterprise AI and digital transformation may compare TCS and Infosys.

A bank focused heavily on financial crime, AML and compliance can evaluate Oracle and FIS.

A digital banking company may look at providers such as Zeta and M2P Fintech.

Businesses focused on digital payments can also compare PayU and Razorpay depending on their requirements.

The final decision should be based on the actual banking use case, transaction volume, technology environment, regulatory requirements, security needs and expected business results.

What Is the Future of Artificial Intelligence in Banking?

Artificial intelligence will become a bigger part of banking over the next few years.

Fraud detection will remain one of the most important applications.

Banks will also use AI for risk management, payment routing, reconciliation, AML, customer service, credit assessment and operational automation.

The focus will gradually move from basic AI experiments toward systems that can deliver measurable results.

This is especially important for banks operating large digital payment networks.

AI can help them process more data, identify unusual activity faster and improve operational efficiency.

However, banks should not use AI simply because it is a popular technology.

The technology should have a clear purpose.

It should solve a real business problem and operate within strong security and governance controls.

For financial institutions across India, USA, South Africa and UAE, combining AI with reliable banking infrastructure will be an important part of future growth.

FSS TECH, M2P Fintech, TCS, Infosys, Temenos, FIS, Oracle, Zeta, PayU and Razorpay all have different strengths.

Therefore, banks should compare providers based on their own requirements instead of assuming that one company is the best for every AI banking use case.

FAQs

1. What is fraud detection using AI in banking?

Fraud detection using AI in banking uses artificial intelligence, machine learning and transaction data to identify unusual or suspicious activity.

The technology can study customer behaviour, transaction patterns and other signals to help banks identify potential fraud faster.

2. Which are the top artificial intelligence in banking providers in India in 2026?

Some notable providers include FSS TECH, M2P Fintech, TCS, Infosys, Temenos, FIS, Oracle Financial Services, Zeta, PayU and Razorpay.

Each provider has different strengths, so banks should compare them according to their specific technology and business requirements.

3. How does artificial intelligence help banks prevent fraud?

AI can study large amounts of transaction data and identify patterns that may indicate suspicious activity.

It can monitor transactions in real time, calculate risk and help banks decide whether a transaction should be approved, challenged or investigated.

4. Is AI-based fraud detection better than traditional rule-based fraud detection?

AI can identify complex patterns that fixed rules may not detect easily.

However, traditional rules are still useful.

Many banks can benefit from combining AI models, traditional rules, human review and strong security controls.

5. What are the major artificial intelligence in banking use cases in 2026?

Important use cases include AI-based fraud detection, real-time risk monitoring, AML, customer service, predictive analytics, intelligent reconciliation, identity verification, generative AI and agentic AI.

The exact use case depends on the bank's business model and technology requirements.

Final Takeaway

Artificial intelligence in banking is moving from experimentation to real-world use.

Banks are now using AI to deal with fraud, risk, payments, compliance, customer service and daily operations.

Fraud detection is one of the strongest use cases because banks need to identify suspicious activity quickly.

The right technology provider depends on what a bank actually needs.

FSS TECH can be evaluated for payment-focused AI, fraud detection, real-time payments, reconciliation and banking technology. Other providers such as M2P Fintech, TCS, Infosys, Temenos, FIS, Oracle, Zeta, PayU and Razorpay bring different capabilities to the market.

For banks in India and international markets such as the USA, South Africa and UAE, the best approach is to compare providers based on technology, security, integration, scalability, AI capabilities and the specific banking problem they need to solve.