The highlights section includes a financial profile summing up all the important financial numbers of the report and relevant business information. The customer wanted to utilize GenAI technology to optimize its business report-building process by summarizing the financial profile of every company.

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• Fraudulent transactions across Europe are an estimated €1.8 billion per annum.
• The number of bank frauds in India was up 166% in FY24.
• In the United States 26% of adults surveyed said they had personally experienced bank/credit fraud.

The explosion of online banking, neobanks, fintechs and financial applications has also made it easier for scammers to strike, making it vital to spot anomalies in transactions and strange behaviour to catch fraud early. In this scenario, Artificial intelligence (AI), Generative AI & Machine Learning (ML) are new sentinels for safe and secure business operations and technology, helping banks, financial services, and insurance (BFSI) companies stay one step ahead of fraudsters.

According to one survey, 62% of UK and US based large/mid-sized businesses intend to deploy AI-based solutions to combat the issue.

Power of AI in fraud detection: Traditional security options vs AI-driven solutions

• AI can “learn” from past fraud cases, helping ML algorithms more accurate with time. An AI model flags suspicious data/anomalies in transactional and behavioural data.
• As self-learning models, AI gets smarter over time, reducing the likelihood of repetitive errors and minimizing false positives.
• AI can not only alert the humans overseeing the systems to potential fraud, but also take action by blocking transactions or removing suspicious attached files.

AI and ML can give banks and financial companies a huge advantage with a “two-steps ahead” approach to security and risk management. For instance, for a large multinational bank, fraud detection traditionally involved wading through mountains of data, reading endless reports, and manually checking every suspicious transaction. It was a slow and painful process, often leading to delays in spotting fraud. Sometimes, customers would even have to report the problem themselves, which could mean losing a lot of money before the bank could fix the issue.

The power of AI-backed fraud prevention means that the same bank can now process copious volumes of data in real time, monitor all activity including transactions as they happen. When a possible high risk event begins to occur, it escalates this to the top of the list for review on priority. The bank can now intervene as the fraud is occurring and prevent it from happening or reduce further potential loss. Overall, this can mean better customer satisfaction with fewer losses incurred.

Using AI in fraud detection means fast detection – since AI algorithms act instantly to freeze/block a transaction/account, and offers increased accuracy over traditional methods – since AI applies dynamic rule setting, learning from itself, rather than just predefined rules. Over time, this results in cost optimization as the long-term cost of prevention vs reaction is lower.

Business impact of AI for fraud prevention

AI can be applied in multiple ways to help mitigate the risk of fraud:

• AI-driven analytics platforms can integrate diverse data sources (financial data, market data, customer data) to provide a comprehensive view of risk exposure.
• GenAI for real-time fraud detection identifies suspicious patterns of behaviour through comprehensive data analysis; this helps block and prevent potentially fraudulent activity. 
• AI-powered alert prioritization is used to classify alerts by risk level, ensuring that higher risk cases get assigned for review and intervention first, which means speedy intervention and protection for the business.
• Predictive analytics help determine future risk based on constantly updated data. AI & ML can minimize false positives, making for a seamless customer experience while ensuring security.
• Data-driven operations backed by AI/ML and robust analytics help ensure regulatory compliance and support KYC verification.
• Automation along with a strong GenAI/AI/ML powered business analytics and data engine supports scalability and boosts operational efficiency.

Use Cases for BFSI | How AI helps in fraud prevention

AI-based use cases for fraud prevention in the banking and financial services sectors can take on various forms:

1. Real-Time Anomaly Detection: Systems using GenAI can detect fraud early by learning normal behaviour and spotting unusual activity or deviations that might indicate account takeovers from identity theft or phishing. This improves speed – something that’s crucial when dealing with fraud, where every minute counts.

GenAI-powered behavioural analysis can monitor app usage, banking transactions, payments, or any other financial transaction across channels/touchpoints in real time and flag off potential threats like unusual spending pattern/unauthorized account access, blocking them and preventing fraud.

AI-backed fraud detection enables faster action, better communication and quick resolution. Traditionally, we have relied on programming languages to identify any aberrations. With ML algorithms, statistical analyses and AI, we can implement a framework that easily identifies the current unusual behaviour as well as new behaviour in the future without too many changes in the program and environment. This translates to cost savings, cuts developer time and reduces time to market/time to go live.
Dipti Pasupalak, Data & Analytics Architect, Cloud Kinetics

2. Automated Fraud Reporting and Reduced Manual Reviews: AI and ML allows for automated fraud reporting and reduces the need for manual reviews. GenAI generates suspicious activity reports (SARs) incorporating millions of data points. With a lower burden on analysts, finance and IT teams, their time can be freed up to be used to propel business growth, enhance solutions and drive innovation.

Automation also makes the process of identifying investment fraud, payment fraud, or card fraud faster, more efficient and often more accurate, with lower instances of false positives.

3. Enhanced Authentication with AI: Secure authentication powered by AI can be improved with GenAI and reduce risk in case of forgery or identity theft.

GenAI can help refine algorithms used for recognition and verification, thereby making traditional biometric verification methods more effective and limiting access to only legitimate users. This cuts the risk of unauthorized access/ account takeover fraud.

Use Case | Seamless User onboarding & authentication with AI/ML-powered solutions from AWS: In the online registration process for an account, using ML-powered facial biometrics – with pre-trained facial recognition and embedded analysis capabilities, ID verification, user onboarding and authentication – can be done securely with no need for prior ML expertise in-house.

4. Detecting Variations in Usage Patterns: AI is able to analyse metadata to detect variations from the norm that might be missed in manual reviews by the human eye. As fraudsters begin to use sophisticated methods including AI to commit fraud, the use of AI as a defense against things like deepfakes will be critical.

Take for example a scenario where a customer has been duped into sharing their net banking details with a fraudster. Normally transactions after this would not be flagged since the data compromise has not occurred on the bank’s system. But AI-based risk monitoring software will spot any unusual pattern in the transactions or amounts not in line with their normal transactions, or even things like screen resolution, currency or language used and flag it for manual tele-verification in real time, more swiftly than older methods.

Use Case | Fraud prevention with Snowflake’s scalable multi-cluster shared data architecture and advanced data governance: This can help protect merchants from fraud and risk. Snowflake-powered fraud prevention models are able to identify bad actors, detect attack vectors and block account takeover attempts.

5. Offline Fraud Prevention: AI-powered video analytics can flag off suspicious behaviour at ATMs and branches that may be linked to ATM skimming, usage of stolen cards or cheque forgery.

Use Case | Geospatial analytics and AI for fraud detection from Databricks: Geospatial data, machine learning and a lakehouse architecture from Databricks help FSI clients better understand customer spending behaviours and spot abnormal credit card transaction patterns in real time. This enhances the fraud prevention and detection capabilities of the organization, which in turn reduces losses and helps cement customer trust.

Building an AI-backed fraud detection strategy

Banks and financial institutions aren’t the only ones with an eye on AI. According to Deloitte’s Center for Financial Services, fraud losses in the United States could hit US$40 billion by 2027, on the back of GenAI.

With fraudsters already using AI, industry needs to quickly adopt an AI-backed defense as well. Here’s your roadmap:

• Create a cross-functional fraud management team: Drawn from IT, operations, compliance, legal, data sciences
• Build a multi-layered fraud detection strategy: Use AI in tandem with traditional anomaly detection systems, encryption, multi-factor authentication etc.
• Implement the right environment and tools: These must be compatible with existing infrastructure and scalable and effective. Banks must modernize their infrastructure to effectively leverage AI for fraud prevention.

By migrating to the cloud or adopting a hybrid approach and establishing a robust data platform, banks can ensure timely access to high-quality data. This real-time data empowers AI, ML, and generative AI systems to analyze patterns, identify potential fraud, and enable rapid intervention.

• Follow transparent & ethical data usage: Adhere to customer privacy norms and practise ethical data usage
• Monitor & update regularly: Retrain with new data to stay effective against new types of fraud
• Run simulations: Run controlled realistic fraud attack simulations to check robustness of the systems in place and keep ahead of advanced fraud attacks

Building an effective AI-backed fraud detection strategy into your organization requires an overall commitment to a security-conscious culture. In addition to the AI, ensure every “human firewall” is well armed to respond to fraudulent activity with regular training and a culture that encourages a security-first approach. Dipti Pasupalak, Data & Analytics Architect, Cloud Kinetics

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The evening was filled with insightful discussions, networking, and a delightful dinner. Participants delved deep into key topics such as:

Uncovering Unparalleled Customer Insights with AI: Attendees explored how to uncover hidden customer trends and preferences, enabling hyper-personalized experiences and enhanced loyalty.

Automating for Efficiency with Modernized Applications: The roundtable highlighted the benefits of modernizing applications to streamline workflows and free up resources for strategic initiatives through the integration of AI and automation tools.

Making Data-Driven Decisions with Modernization: We discussed the importance of gaining a clear view of data through modernization, allowing organizations to translate raw data into actionable insights for smarter, faster decision-making.

Predicting & Preventing Downtime with Modern Infrastructure: Participants learned how leveraging AI and modernizing legacy infrastructure can lead to predictive maintenance, ensuring smooth operations and minimizing disruptions.

Unlocking App Modernization Potential: The event also covered how app modernization enhances agility and scalability, enabling businesses to quickly adapt to changing market demands and deliver superior customer experiences.

This exclusive event provided a unique platform to share best practices, network with peers, and explore strategies for unlocking the full potential of organizations.

The post Unlock Business Growth with Modernization & GenAI-Powered Data Analytics appeared first on Cloud Kinetics.

The organization maintained a robust on-premises IT infrastructure distributed across two data centres, balancing homegrown and legacy applications. To optimize operational efficiency and ensure reliability,  they hosted their environment in a colocation data centre. Despite grappling with legacy systems, the customer remained proactive in continuous technology refresh cycles to stay abreast of evolving industry standards.

As part of its commitment to offering the best of the best in services and solutions, the organization had lined up a shift from its traditional on-premises IT infrastructure to one that would leverage the public cloud and usher in a phase of digital transformation. With recent acquisitions of several regional fintech companies and strategic partnerships with other fintech platforms in the works, they would benefit from the business agility, data visualization and analytics capabilities, and the quicker go-to-market that a modern backbone would bring. And so began the search for the right partner for this cloud journey.

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The evening was filled with insightful discussions, networking, and a delightful dinner. Participants delved deep into key topics such as:

• Uncovering Unparalleled Customer Insights with AI: Attendees explored how to uncover hidden customer trends and preferences, enabling hyper-personalized experiences and enhanced loyalty.
• Automating for Efficiency with Modernized Applications: The roundtable highlighted the benefits of modernizing applications to streamline workflows and free up resources for strategic initiatives through the integration of AI and automation tools.
• Making Data-Driven Decisions with Modernization: We discussed the importance of gaining a clear view of data through modernization, allowing organizations to translate raw data into actionable insights for smarter, faster decision-making.
• Predicting & Preventing Downtime with Modern Infrastructure: Participants learned how leveraging AI and modernizing legacy infrastructure can lead to predictive maintenance, ensuring smooth operations and minimizing disruptions.
• Unlocking App Modernization Potential: The event also covered how app modernization enhances agility and scalability, enabling businesses to quickly adapt to changing market demands and deliver superior customer experiences.

This exclusive event provided a unique platform to share best practices, network with peers, and explore strategies for unlocking the full potential of organizations.

We are grateful for the active participation and contributions of everyone who joined us for this impactful event.

The post CXO Roundtable:<br/> Unlock Business Growth with Modernization, Data Analytics & Gen AI appeared first on Cloud Kinetics.

We are thrilled to have connected with forward-thinking professionals committed to advancing digital transformation in the FSI sector.

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It was an energizing experience to explore the potential of automation alongside such a distinguished audience.

Key Takeaways: Empowering FSI Enterprises Through Automation

The event delved into how automated data management can be a game-changer for financial services enterprises. Here’s a recap of the key themes we explored:

• Boosting Efficiency: We discussed how automating manual data tasks can free up valuable time for your team, allowing them to focus on strategic initiatives that drive growth.
• Enhancing Security: The session explored how robust access controls and encryption, facilitated by automated data management, can strengthen your data protection posture.
• Simplifying Compliance: We highlighted how automated data governance and audit trails can streamline compliance efforts, ensuring adherence to ever-evolving regulations.
• Powered Decision-Making: The event showcased the power of AI-powered analytics fueled by automated data. This combination empowers FSI firms with deeper insights for fraud detection, risk management, and more informed decision-making.

Looking Forward: The Future of FSI and Automation

The event fostered a dynamic discussion on the transformative potential of automation in the financial services industry. We were impressed by the insightful questions and engaging conversations throughout the session.

Stay tuned for future events!

The post AI-Powered Insights For FSI – Powered By AWS Data Services appeared first on Cloud Kinetics.

For traditional banks this doubles up as an opportunity and an urgent reminder. To compete effectively with digital banks and fintechs and thrive in this evolving financial ecosystem, senior IT/business decision-makers at established banks must prioritize modernizing legacy systems and embracing the Open Banking paradigm.

Advancing customer experience with open banking

Open banking ushers in a new era of convenience and financial control. It fosters financial inclusion, more personalized services and convenience for customers. Open banking also increases the competitiveness of banks by enabling them to provide a diverse suite of financial products & services at reduced operational costs.

Some open banking use cases include:

• Instant payments and fund transfers directly from third-party applications such as PayNow in Singapore and Unified Payments Interface (UPI) in India.
• Account aggregation features for customers to access and manage savings & deposit accounts, loans, credit cards and investments from a single application.

From monolith to microservices: The application modernization journey to open banking

For most banks, successful adoption of open-banking standards will mean substantial re-architecting of their current application estate and IT infrastructure, and accelerated integration with third parties to on-board new products & services from partners. This is a critical success factor in the open banking ecosystem.

To seamlessly integrate with various third parties through APIs, banks will require an enterprise-wide adoption of microservices and API-based architecture to consume data from core banking and other legacy backend systems. This will support agile delivery, provide scalability and flexibility with multi-cloud / hybrid cloud deployments.

A microservices-based application modernization approach will eventually enable banks to drive growth through new business models, collaborate with partners to quickly on-board new product offerings and enhance customer experience.

1. Building the optimal microservices-based architecture for open banking

The application modernization journey and timelines will depend on the product roadmap, technology readiness and current IT architecture of the bank.

Banks with monolithic applications, legacy core banking systems and legacy integration platforms will need to adopt a phased approach to minimize business risks and potential disruptions. A phased approach will also allow banks to on-board partners and build marketplace offerings like digital wallets, lending and insurance services for customers in an incremental manner.

The figure here below illustrates an indicative microservices-based reference architecture for a bank along with key components such as API gateway, service orchestrator and microservices-based core services.

• The first step in the application modernization journey would involve building a service orchestration layer (Figure 1, point# 2) to efficiently manage commonly used cross-cutting concerns and non-functional requirements such as security, configurations, log aggregation, distributed tracing, service discovery and circuit breaker.
• All the above services can be developed as common components designed to ensure the core business logic is separated from cross-cutting non-functional concerns.
• The service orchestration layer along with an API gateway will interface with the core banking platform and expose APIs to both internal and external parties for accelerated integration.
• In subsequent phases, banks can start decomposing their monolithic core applications using various Microservices design patterns such as Strangler and Sidecar aligned to product domains such as account services, payment services and underwriting services (Figure 1: point# 3). For example, account services would include features such as account opening, customer onboarding and account updates.

2. Microservices-based application modernization: Challenges & way forward

Transitioning from a monolith to microservices can be a complex but rewarding undertaking for banks. Often, banks are concerned about technical complexity, security challenges and change management.

The right application enablement partner will equip you with a comprehensive roadmap, from initial strategy and architecture design to seamless implementation and ongoing support, ensuring your microservices journey is smooth and efficient.

3. Modernizing banking applications with Cloud Kinetics

Cloud Kinetics helps banking & financial services customers to amplify the value of cloud with our Application Modernization approach, driving business agility & flexibility.

Our modernization competencies focus on Application Portfolio Assessment, Microservices Architecture, Serverless, API Integration, Security & Compliance combined with DevOps & Continuous Delivery Approach. Our strategic partnerships with industry leaders like AWS, Azure, GCP (hyperscalers), Kong (API gateway), CAST (portfolio assessment) enable us to deliver a seamless solution for our customers.

Customer impact story: App modernization, cloud-native design & a one-stop digital marketplace for a leading fintech

Our customer is a leading fintech company that facilitates financing through credit guarantee schemes for micro, small & medium enterprises (MSMEs). They felt their current platform was lacking the comprehensiveness, accessibility and user-friendly experience that MSMEs now demand in a digital-first world.

With Cloud Kinetics, the customer embarked on a transformation journey to modernize their platform and provide MSMEs with a one-stop digital marketplace, with opportunities for cross- and up-selling products and services.

Cloud Kinetics developed the digital lending platform on AWS, using microservices architecture to create a cloud-native lending platform that was secure, scalable and adaptable to evolving needs. Cloud Kinetics also built a digital marketplace for the customer that expanded beyond loans, offering MSMEs financial products like working capital solutions, insurance options, and seamless integration with credit guarantee schemes. The interface enabled the bank to offer MSMEs a more intuitive experience and better manage their financial health.

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