Generative AI - Problem Statements & Solutions
Delays in technical info and recommendations caused inefficiencies and dissatisfaction.
Solution Deployed:
- Developed a RAG-enhanced real-time assistant integrated with WhatsApp.
- Combined generative AI with real-time retrieval from a curated knowledge base.
- Provided instant answers, product recommendations, and troubleshooting guides.
Benefits Delivered:
- Reduced query resolution time by 40%, boosting customer satisfaction.
- Increased product sales through accurate and timely recommendations.
- Easy access to information, available 24/7 via WhatsApp, powered by GPT technology.
Ensuring accuracy and compliance in reports while reducing manual verification effort.
Solution Deployed:
- Developed and implemented a Generative AI-powered fact-checking tool.
- The tool automatically verifies data and statements in annual, quarterly, and ESG reports.
- Speeds up the verification process, reducing manual oversight.
Benefits Delivered:
- Enhanced report accuracy by 30%, minimising human errors in the verification process.
- Significantly shortened the report review cycle, reducing it from 3 weeks to just 3-5 days.
- Improved overall efficiency and compliance, streamlining the reporting process.
Need for faster incident classification and prioritisation to improve safety and compliance.
Solution Deployed:
- Implemented an AI-driven incident classification system.
- Leveraged generative AI to analyse and categorise incidents, identifying hazards and serious incidents.
- Prioritised incidents for immediate attention, improving safety responses.
Benefits Delivered:
- Reduced incident classification time by 50%, speeding up response.
- Enhanced safety by identifying high-risk patterns in operational data.
- Strengthened compliance with safety regulations, reducing liabilities.
Employee queries on HR policies, benefits, and leave management often go unanswered or delayed
Solution Deployed:
- Developed a Generative AI-based HR assistant integrated with MS Teams.
- Provided instant, personalised responses on policies, benefits, payroll, and leave management.
- Enabled the assistant to resolve queries autonomously, improving response times.
Benefits Delivered:
- Resolved 70% of queries without human intervention, speeding up responses.
- Enhanced employee satisfaction through accurate and timely support.
- Allowed HR teams to focus on strategic initiatives by offloading repetitive tasks.
Machine Learning - Problem Statements & Solutions
Inaccurate trading volume forecasts hindered resource allocation and market positioning.
Solution Deployed:
- Developed a machine learning-based trading volume forecasting model.
- Segmented by client and product categories.
- Incorporated macroeconomic indicators, historical patterns, and global trends.
- Delivered accurate predictions for trading volumes.
Benefits Delivered:
- Improved forecast accuracy by 40%, enhancing resource allocation for trading desks.
- Enabled actionable insights for strategic decision-making and market positioning.
- Optimised client segmentation and product targeting, increasing trading efficiency.
Forecasting cashflow and liquidity accurately is challenging, leading to poor financial planning
Solution Deployed:
- Built a machine learning-based cashflow and liquidity forecasting model.
- Analysed historical financial data, payment patterns, and economic indicators.
- Provided real-time forecasts and scenario analyses to optimise cashflow management.
Benefits Delivered:
- Reduced forecasting errors by 25%, improving financial planning accuracy.
- Enhanced liquidity management, minimising idle cash and lowering borrowing costs.
- Supported informed decision-making with predictive insights for various economic scenarios.
Inefficient pricing strategies led to suboptimal revenue and competitive positioning.
Solution Deployed:
- Implemented a dynamic pricing engine using machine learning.
- Considered competitor pricing, customer demand, and market trends.
- Delivered real-time pricing recommendations to maximise revenue and competitiveness.
Benefits Delivered:
- Increased revenue by 15% through optimised pricing strategies.
- Enhanced competitiveness with dynamic pricing adjustments based on competitor behaviour.
- Improved customer satisfaction and retention by offering competitive fuel prices.
Inaccurate demand forecasting led to stockouts, overstock, and inefficient inventory management.
Solution Deployed:
- Developed a machine learning-based demand forecasting model.
- Predicted weekly demand at the SKU and warehouse level across the USA.
- Used historical sales, seasonality, weather, and market data to enhance forecast accuracy.
Benefits Delivered:
- Improved forecast accuracy by 35%, reducing stockouts and overstock.
- Optimised inventory management, lowering holding costs by 20%.
- Supported better production planning and distribution strategies.
Automation - Problem Statement & Solutions
Manual client onboarding processes were time-consuming and error-prone, impacting efficiency.
Solution Deployed:
- Designed and implemented an end-to-end client onboarding automation workflow.
- Integrated application intake, KYC, CDD, AML checks, credit assessment, and underwriting.
- Utilised web applications, RPA, workflow orchestration, and AI-driven document processing.
Benefits Delivered:
- Reduced onboarding time by 50%, enhancing customer experience.
- Cut manual errors in compliance checks by 30%.
- Increased operational efficiency by freeing up employees for higher-value tasks.
Conventional invoice processing was slow and prone to errors, resulting in delays and inefficiencies
Solution Deployed:
- Developed an automated accounts payable system to streamline invoice processing.
- Used OCR to scan invoices and machine learning to identify duplicate entries.
- Implemented RPA to automatically assign invoices to budget holders and accounts teams.
- Automated payment instructions following approval, reducing manual effort.
Benefits Delivered:
- Reduced invoice processing time by 60%, ensuring timely payments.
- Cut manual intervention by 40%, reducing errors and increasing efficiency.
- Improved cash flow visibility and strengthened vendor relationships with timely payments.
Traditional procurement processes led to delays in managing orders and resolving supplier queries.
Solution Deployed:
- Implemented a procurement process automation system.
- Managed goods/services procurement orders and responded to supplier queries.
- Automated communication channels and workflows for query resolution and escalation.
Benefits Delivered:
- Accelerated query resolution time by 50%, improving supplier satisfaction.
- Enhanced compliance with procurement policies through streamlined workflows.
- Reduced administrative workload, allowing the team to focus on strategic sourcing.
Manual mortgage application processes caused delays and increased error rates.
Solution Deployed:
- Deployed an automation solution for the mortgage application lifecycle.
- Included document collection, credit scoring, income verification, underwriting, and approval.
- Integrated AI-powered document processing, rule-based engines, and RPA for stakeholder coordination.
Benefits Delivered:
- Reduced mortgage approval time by 40%, improving customer satisfaction.
- Enhanced compliance with regulatory standards through automated checks.
- Minimized manual errors, ensuring accurate and efficient decision-making.
Data Engineering - Problem Statement & Solutions
Disjointed data systems hindered real-time decision-making and operational efficiency.
Solution Deployed:
- Developed and implemented a robust Data and AI Strategy.
- Built a centralized cloud-based analytics platform integrating trading, back-office, risk, and market data systems.
- Implemented Data Management and Governance tools for self-service data access and data-driven decision-making.
Benefits Delivered:
- Created a unified platform for real-time and historical data analysis, improving decision-making.
- Reduced data silos, enhancing collaboration across trading, risk, and operations teams.
- Empowered business users with self-service analytics, boosting operational efficiency and agility.
Fragmented data from trading, client interactions, and risk systems hindered insights and monitoring
Solution Deployed:
- Designed and implemented a data warehousing solution to consolidate trading, client, and risk data.
- Provided real-time insights into client activity, automated risk monitoring, and generated client statements.
Benefits Delivered:
- Enabled real-time monitoring of client activity, reducing risk exposure by 25%.
- Automated client statement generation, improving accuracy and timeliness.
- Streamlined risk management processes, ensuring compliance with regulatory standards.
Fragmented data limited accurate analysis and stress testing of trading strategies.
Solution Deployed:
- Developed a data engineering solution to create a time-series dataset.
- Consolidated trading positions, market prices, risks, and liquidity data.
- Enabled detailed historical analysis and stress testing for market resilience and strategy evaluation.
Benefits Delivered:
- Improved accuracy of stress-testing models by 30%.
- Provided deeper insights into historical trading behaviours and market conditions.
- Strengthened risk management with a robust data foundation for scenario modelling.
Disparate data sources slowed inventory tracking, product recommendations, and order fulfilment.
Solution Deployed:
- Implemented a scalable data engineering pipeline to process and analyse data from multiple sources.
- Integrated website interactions, inventory systems, and customer behaviour analytics.
- Enabled real-time inventory tracking, personalised recommendations, and streamlined order fulfilment.
Benefits Delivered:
- Improved inventory accuracy by 40%, reducing stockouts and overstock.
- Enhanced customer satisfaction with personalised recommendations and faster delivery.
- Reduced data processing time by 50%, providing real-time insights for marketing and sales teams.