The financial industry is going through a major shift, with artificial intelligence (AI), machine learning (ML), and cloud technology revolutionizing how investment firms operate. Moving away from outdated manual processes and fragmented data, investment firms are embracing intelligent automation, real-time analytics, and predictive models, with platforms like ServiceNow AI & ML, ServiceNow Workflow Automation, and Amazon Web Services (AWS) at the helm of this transformation.
Today, we’re joined by Sasibhushan Rao Chanthati, a financial technology expert and senior software engineer at a global investment firm in Owings Mills, Maryland. Recognized for his outstanding contributions and performance in information technology, Sasi is also an author of several publications on AI, ML, and large language models, with notable works on ServiceNow and AWS. He also mentors in top organizations.
Sasibhushan Rao Chanthati is slated to speak at the upcoming Data Science Salon (DSS) conference in New York, focusing on the “Future of AI & ML in Finance.” Scheduled for May 15, 2025, this event is supported by the S&P Global and ArangoDB.
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Artificial intelligence and machine learning are transforming financial services. How is Artificial Intelligence revolutionizing organizations?
AI is reshaping the financial services industry by enhancing decision making, improving risk management, automating fraud detection, and personalizing investment strategies. Financial firms leverage AI and ML to process vast amounts of market data, identify patterns, and make data driven predictions that were previously impossible through traditional methods. One key area where AI is making an impact is algorithmic trading. AI powered trading algorithms can analyze real-time market data, detect patterns, and execute trades within milliseconds far beyond human capabilities. For instance, hedge funds and asset managers use AI driven quant models and based on the predictions provided by AI firms can adjust portfolios dynamically. Another major application is fraud detection and risk assessment. AI models can analyze transaction data, detect anomalies, and flag potential fraud before it happens.
Any specific examples?
For example, AI powered fraud detection systems used by banks analyze spending patterns and trigger alerts when unusual transactions occur like a sudden large purchase from a foreign country. AI is also revolutionizing personalized financial advisory services. Traditional investment firms are integrating AI chatbots and robo-advisors that use ML algorithms to provide personalized investment recommendations based on a client’s financial goals, risk tolerance, and market conditions.
How is AI transforming beyond investment in financial industries?
Beyond investments, AI is streamlining regulatory compliance and risk mitigation. Financial firms deal with vast amounts of regulatory data, and AI can automate compliance checks by analyzing legal documents and ensuring adherence to financial regulations. This reduces the manual burden on compliance teams and minimizes the risk of penalties. AI is revolutionizing financial firms by optimizing trading strategies, enhancing fraud prevention, personalizing investment services, automating compliance, and improving overall operational efficiency. With continuous advancements in machine learning and big data analytics, AI will play an even bigger role in shaping the future of financial services.
Can AI-powered solutions fully replace traditional investment strategies, or do you think a balance is needed?
AI is a powerful augmentation tool rather than a complete replacement; it totally depends on how we train based on the business use case. While AI can process large datasets, identify patterns, and automate trading strategies, human expertise is still vital for interpreting market nuances, handling unpredictable events, and ensuring ethical decision-making. The best approach is a hybrid model AI-driven insights combined with human expertise to make informed investment decisions.
How is ServiceNow improving financial operations in asset management?
ServiceNow has become a critical platform in financial investment operations, helping firms streamline processes, enhance compliance, and improve operational efficiency. By leveraging workflow automation and custom applications, investment firms can optimize their trade management, risk assessment, compliance tracking, and client servicing areas that traditionally involve significant manual effort and regulatory scrutiny. Investment firms handle a variety of workflows, including investments decisions approvals, portfolio rebalancing, investor onboarding, risk assessment, and regulatory reporting. ServiceNow’s automation engine allows financial organizations to build custom workflows that integrate seamlessly across departments and external systems.
Any specific examples?
For example, a Custom Compliance Monitoring App built on ServiceNow can: Track transactions and flag potential compliance violations based on predefined rules. Generate real-time reports for auditors and regulators to ensure transparency. Integrate with external risk assessment platforms (Bloomberg, Reuters, etc.) for seamless data flow. Similarly, firms use automated risk assessment workflows to: Evaluate investment risks based on market conditions. Ensure that transactions comply with risk appetite frameworks. Automatically escalate high-risk trades for further review.
How can ServiceNow automate the investment process?
ServiceNow can automate: Investment compliance checks ensuring the investment meets regulatory requirements before execution. Approval workflows Routing trade requests to the appropriate risk managers for review and approval. Real-time notifications, notifying portfolio managers, and compliance officers about approvals or required actions. Custom Applications can be developed for Risk Management & Compliance. Financial investment firms must adhere to strict regulatory frameworks such as SEC, FINRA, MiFID II, and Basel III. ServiceNow enables firms to build custom risk management applications that help monitor compliance in real-time.
How are ServiceNow ITOM and ITSM useful in fintech?
Streamlining IT Operations & Incident Management for Trading Platforms: Investment firms rely on robust trading platforms and financial data systems that must operate with near-zero downtime. ServiceNow’s IT Service Management (ITSM) and IT Operations Management (ITOM) help firms proactively monitor and resolve system issues before they impact trading operations. Automated incident response workflows detect and resolve system failures, reducing downtime. Predictive AI-powered analytics identify potential system bottlenecks before they impact trading. Change management automation ensures seamless software updates for trading platforms without disrupting operations.
How is financial data protected?
Data Integration & Interoperability in Financial Ecosystems: Investment firms often use multiple systems, including portfolio management tools, trading platforms, CRM systems, and regulatory databases. ServiceNow’s Integration Hub allows firms to: Seamlessly connect external systems like Bloomberg, Thomson Reuters, or proprietary investment platforms. Automate data reconciliation between front-office and back-office operations. Enable real-time portfolio tracking and performance reporting.
The biggest challenges are Legacy infrastructure – Many financial firms still use old systems, making data integrations more complex. Regulatory compliance, the decisions must align with strict financial regulations. Data privacy & security, Handling PII data like sensitive customer and financial data securely is a major priority. To overcome these, firms should adopt cloud-native architectures, ensure explainable AI models, and implement robust cybersecurity measures.
Cloud adoption is growing in fintech. How do Amazon Web Services and cloud computing improve security and scalability in financial services?
AWS is a game-changer for fintech because it provides: Scalability – Firms can process vast amounts of transactions without performance lags. Security – AWS offers encryption, access controls, and AI-driven threat detection to protect sensitive financial data. Disaster Recovery – Financial institutions rely on AWS’s multi-region backups to ensure business continuity. Cloud adoption also enables faster AI model training, seamless integrations, and cost savings compared to traditional on-premises infrastructure.
Cloud costs are a major concern for enterprises. Can you share insights from your research on how businesses can effectively reduce cloud expenses?
As described, the cloud cost optimization requires a strategic approach that balances performance, scalability, and budget efficiency. My research highlights several key strategies: Right-Sizing Cloud Resources – Many organizations over-provision their cloud resources. By analyzing actual usage patterns and selecting the optimal instance sizes, companies can significantly reduce costs. Leveraging Reserved & Spot Instances – Instead of using expensive on-demand pricing, firms can take advantage of AWS Reserved Instances, Azure Savings Plans, or Google Cloud Committed Use Discounts for long-term workloads. Serverless & Auto-Scaling Architectures – Moving to serverless computing (AWS Lambda, Azure Functions) eliminates idle resource costs, and auto-scaling ensures that businesses only pay for what they use. Optimizing Storage Costs – Businesses often store unused or duplicate data in expensive storage tiers. Implementing lifecycle policies in AWS S3, Azure Blob, and GCP Cloud Storage can automatically move infrequently accessed data to cheaper archival storage. Cloud Cost Monitoring & FinOps – Tools like AWS Cost Explorer, Azure Cost Management, and FinOps methodologies help businesses monitor spending, allocate budgets efficiently, and forecast future cloud expenses. For example, a financial services firm reduced its cloud bill after implementing an auto-scaling and right-sizing strategy, ensuring they only paid for the resources they needed.
What are the biggest challenges enterprises face when migrating to the cloud, and how can they overcome them?
Cloud migration is a complex process that requires careful planning to avoid downtime, security risks, and cost overruns. The main challenges include Legacy System Compatibility – Many enterprises run legacy applications that are not cloud-ready. A phased approach, such as re-platforming or refactoring, is often needed instead of a direct lift-and-shift. Security & Compliance Risks – Financial, healthcare, and government industries must ensure compliance with GDPR, HIPAA, PCI-DSS, etc. Best practices include data encryption, IAM policies, and continuous compliance monitoring. Cost Overruns & Unplanned Expenses – Many companies experience cost spikes post-migration due to inefficient cloud resource allocation. Pre-migration cost analysis and automated scaling solutions help manage expenses effectively. Downtime & Performance Issues – A poorly planned migration can lead to service disruptions. Businesses should implement a hybrid cloud approach or multi-cloud redundancy to maintain continuity during the transition. To overcome these challenges, businesses should follow a structured cloud migration framework that includes Assessment & Planning – Evaluating workloads and choosing the right migration strategy (Lift-and-Shift, Re-platform, Refactor). Pilot Migration & Testing – Running test migrations to detect potential performance issues before full-scale implementation. Automation & Optimization Post-Migration – Implementing cloud-native services like auto-scaling, managed databases, and cost monitoring tools to ensure an optimized cloud environment.
Different industries have different cloud adoption patterns. How are industries like finance, healthcare, and retail leveraging cloud migration?
Cloud adoption varies significantly across industries due to regulatory requirements, data sensitivity, and operational demands. Here’s how different industries can leverage cloud migration: Finance & Banking – Using cloud-based trading platforms for high-frequency trading, implementing AI-driven fraud detection, and ensuring compliance with SOX, PCI-DSS, and GDPR. Healthcare – Migrating electronic health records (EHRs) to HIPAA-compliant cloud storage, using AI-powered diagnostics and telemedicine platforms, and implementing blockchain-based security for data integrity. Retail & E-Commerce – Using cloud-based inventory management, deploying AI-driven recommendation engines, and running multi-cloud strategies to ensure high availability during peak shopping seasons.
What are the latest trends in cloud engineering that businesses should be ready for?
The cloud landscape is evolving with several key trends likely: AI-Powered Cloud Optimization – AI-driven cost analytics and automated scaling are making cloud management more efficient. Multi-Cloud & Hybrid Cloud Strategies – Organizations are leveraging multi-cloud environments to reduce vendor lock-in and improve reliability. Edge Computing & Serverless Expansion – Edge computing is growing, allowing businesses to move workloads closer to users for faster processing and lower latency. Cloud Security & Zero-Trust Architecture – Zero-trust security models and confidential computing are enhancing cloud security for enterprises.
Indeed, it seems the age of intelligent finance is here. Any final thoughts?
The financial industry is at the dawn of a new era, where investment operations are no longer managed manually but driven by AI-powered decision-making. Real-time, automated compliance enforcement. Cloud-first, scalable investment data ecosystems. This is not just about keeping up with technology, it’s about leading the future of finance. The firms that embrace AI, ServiceNow automation, and AWS-powered financial intelligence will define the next generation of investment success.
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