Imagine you’re a compliance analyst at a retail bank. You’ve just been asked to submit a regulatory report by the end of the day. This means bringing together data spread across multiple systems, checking transaction data, reconciling customer records across departments, scanning email threads for policy updates, and more. Then, double-check every number against the latest Basel III requirements. Once you finally have all the data, you need to compile the report, get it reviewed, and send it to the regulator — all without a single error.
Now, picture this instead: the data pulls itself together, relevant risks are flagged in real time, and the report drafts itself. You simply review and hit send.
This kind of seamless orchestration and automation has become a reality with the right tools and platforms. By connecting disparate data, automating workflows, and embedding intelligence into decision-making, organizations can reduce complexity and improve productivity.
At Tiger Analytics, we focus on three foundational pillars when building an analytics solution that is optimized and streamlined for efficiency and effectiveness:
- Reliable data
- Creating extensive data lakes with new governance processes
- Setting up connectors across data applications
- Strong analytics engine
- Managing and maintaining the diverse dependencies as the solution evolves
- Improvements to the framework (Agentic or otherwise) or dependencies
- Efficient implementation
- Identifying the right set of tools/framework dependencies to onboard
- Identifying internal/external data dependencies
- Building orchestration and governance on those dependencies
Over the years, we used this foundation to build accelerators and SOPs, which connect business needs with domain context and technical knowledge, for faster and smoother implementation.
Agentic AI platforms like Google Agentspace follow the same principles to help users across the enterprise quickly access information from various sources, summarize and understand the data, and take action with the help of AI agents. They provide the flexibility to integrate custom accelerators and SOPs, along with the structure needed to set up custom governance frameworks that ensure integrity throughout the decision-making process.
In this blog, we explore regulatory compliance in retail banking and demand forecasting in retail, and the role AI agents can play in reducing risk and improving outcomes.
Use case 1: Automated regulatory reporting and compliance monitoring in retail banking
Retail banks navigate a maze of compliance and regulatory requirements such as Basel III, GDPR, and more. As of 2023, 88% of global companies said GDPR compliance alone costs their organization more than $1 million annually, while 40% spend more than $10 million. These regulations demand extreme diligence with a focus on transparency, accuracy, and timeliness.
Traditionally, banks rely on multiple systems to collect and report data:
- ERP systems for financials
- CRM tools for customer data
- Core banking systems for transaction histories
- Email communication for policy updates and legal notices
Manually piecing together fragmented data is a time-consuming and error-prone process that may expose the organization to compliance risks. Here’s where we believe Agentic AI can add value:
- Seamless data integration: Platforms such as Agentspace integrate data from various sources, including email communications, using prebuilt 2P and 3P connectors for easy aggregation and standardization of data as required for regulatory reports. This eliminates the need for manual data entry.
- Real-time compliance monitoring: Custom AI agents can be built and orchestrated to continuously monitor and analyze compliance-related data against industry standards, such as Basel III and GDPR for up-to-date reports. For example, Jira/Salesforce can be seamlessly connected with Agentspace applications through prebuilt connectors, and processes can be tracked to ensure proper compliance with policy
- Timely report generation and submission: AI agents can help automate the process of preparing and submitting Basel III liquidity reports, capital adequacy reports, and GDPR compliance documents, saving time and improving efficiency.
- Audit-ready reporting: Integrating prebuilt 2P and 3P connectors with enterprise solutions ensures every data point, action, and process is tracked, so organizations are always prepared for external audits. These comprehensive audit trails also provide much-needed transparency, thereby reducing the risk of penalties due to non-compliance.
Automating regulatory reporting helps retail banks reduce manual effort, cut down on compliance costs, and meet reporting deadlines more efficiently. Real-time monitoring and built-in validation minimize risk exposure while keeping pace with evolving regulations. In addition, the process becomes fully traceable and audit-ready by design.
Use case 2: Integrated demand forecasting & inventory management in retail
With fluctuating market conditions, changing consumer demands, and growing competition, retailers are finding new opportunities to improve operations by leveraging AI. According to a 2024 Deloitte report, 6 in 10 retail buyers in the US said that AI-enabled tools enhanced demand forecasting and inventory management. As expectations for hyperpersonalized experiences and seamless omnichannel shopping grow, AI can help retailers remain agile and respond effectively.
Traditional demand forecasting in retail is often based solely on historical sales data and fails to account for external factors such as weather, economic conditions, or cultural trends that could impact consumer behavior. As a result, retailers can face challenges managing inventory across both online and brick-and-mortar stores, leading to overstocking, stockouts, or lost sales. Here’s how AI agents can help:
- External factor integration: In addition to integrating data sources spread across the business and every touchpoint, AI agents also enable integration of external data sources such as weather forecasts, local events, trends on social media platforms, etc. This provides retailers with a holistic view of inventory and demand, and helps proactively adjust inventory levels based on real-time external conditions.
- AI-driven demand forecasting: Machine learning algorithms analyze historical sales data, customer preferences, weather patterns, economic conditions, and social media trends to predict demand with high accuracy across multiple product categories and geographic regions.
- Real-time inventory optimization: AI agents help retailers track inventory across both online and offline channels and automatically adjust stock levels based on demand forecasts. For example, if a popular product is selling faster than expected in an online store, agents can trigger automatic inventory replenishment from physical stores or external suppliers. They also enable cross-channel inventory synchronization so products are available where customers want to buy them.
With AI-driven demand forecasting enhancing forecast accuracy, retailers can reduce instances of stockouts and overstocking. This optimization lowers storage and stockholding costs and improves customer satisfaction, ensuring the right products are available at the right time. Real-time data collection and analysis help retailers make faster, more informed decisions, boosting agility and driving better business performance.
In summary
Any analytical solution is only as strong as the underlying data. That’s why every large-scale analytics transformation must begin with a robust data infrastructure and quality control. As businesses adopt large language models and Agentic frameworks, the focus shifts to ease of adoption and driving measurable outcomes at scale. To remain competitive, companies must automate complex processes and connect fragmented data sources. Platforms like Agentspace, with its multi-agent architecture, help facilitate efficient data flow, adaptive learning, and improved decision-making.
Governance is crucial throughout the deployment, operation, and scaling of AI agents. A structured approach that combines ‘human-in-the-loop’ oversight and clear operational guardrails ensures integrity and compliance of agents and agentic frameworks, aligning them with organizational objectives while maintaining ethical standards.
References
https://www.pwc.com/us/en/services/consulting/cybersecurity-risk-regulatory/library/privacy-reset.html
https://www2.deloitte.com/us/en/insights/industry/retail-distribution/retail-distribution-industry-outlook.html
