AI Challenges in Finance:

• 🤥 Misleading advice
• 🔍 Incorrect risk assessments
• 🔒 Sensitive information
• 🚫 Hallucination mitigation (solved by Retrieval Augmented Generation - RAG)
• 📊 Data quality, relevance, and accuracy

AI in Non-Card Payments:

• 📚 Historic "stock" of transaction records
• 🌊 "Flow" in the form of live data

Cash Flow Analysis:

• 🔮 Forecasting: real-time insights into cash position
• 💰 Working capital optimization: liquidity management, recommendations to corporate treasurers, value-adding
• 🔍 Payment processing optimization: automate workflow
• 🚨 Risk and fraud: transaction screening, avoid manual intervention

Data-Led Value-Added Services:

• 🧠 Customized services and solutions for corporate treasurers and senior finance executives
• 🌍 Tailored offerings based on business verticals and regions

Real-Time Data Visibility and Forecasts:

• 🔍 Offer real-time visibility into cash positions
• 📊 Provide cross-institution dashboards for comprehensive data insights

Value-Adding Data Insights:

• 🔮 Deliver scenario-based forecasting capabilities
• 🤖 Offer recommended actions based on data analysis
• 💯 Generate risk scores on future positions to aid decision-making

Improved Payment Services:

• 🤖 Automate payment tracking and reconciliation processes
• ⚡️ Enhance efficiency and accuracy of payment-related services

Central Dashboard:

• 💻 Real-time consolidated data from multiple banks into a single dashboard
• 💰 Real-time cash forecasting and balances
• 🛡️ Fraud protection
• 📊 Custom reporting for internal use
• 💳 Receivables reconciliation
• 📄 SO20022 compliance
• 🔍 Data analytics (risk scores on positions)

Central Dashboard with Map Data:

• 🌍 Latitude and longitude coordinates
• 📍 Text with names of geographical areas (countries, states)
• 🗺️ Choropleth: predefined shapes for geographical areas
• 📍 Scatter: data markers to indicate data points
• 🔥 Heatmap: color intensity of data points

More MongoDB Advantages:

• 🔍 Binary JSON format (BSON): faster parsing, searched and indexed
• ⚡️ Increasing performance for ad-hoc queries
• 🔍 Significant difference at scale for field queries, range queries, regular expression searches
• 💾 Point-in-time recovery: adopt MongoDB's operation log (oplog), continuous backup, consistent snapshots

Editor

Danny Chan, specialty of FSI and Serverless

Kenny Chan, specialty of FSI and Machine Learning