AI Challenges in Finance:
- ๐คฅ Misleading advice
- ๐ Incorrect risk assessments
- ๐ Sensitive information
- ๐ซ Hallucination mitigation (solved by Retrieval Augmented Generation – RAG)
- ๐ Data quality, relevance, and accuracy
AI in Non-Card Payments:
- ๐ Historic “stock” of transaction records
- ๐ “Flow” in the form of live data
Cash Flow Analysis:
- ๐ฎ Forecasting: real-time insights into cash position
- ๐ฐ Working capital optimization: liquidity management, recommendations to corporate treasurers, value-adding
- ๐ Payment processing optimization: automate workflow
- ๐จ Risk and fraud: transaction screening, avoid manual intervention
Data-Led Value-Added Services:
- ๐ง Customized services and solutions for corporate treasurers and senior finance executives
- ๐ Tailored offerings based on business verticals and regions
Real-Time Data Visibility and Forecasts:
- ๐ Offer real-time visibility into cash positions
- ๐ Provide cross-institution dashboards for comprehensive data insights
Value-Adding Data Insights:
- ๐ฎ Deliver scenario-based forecasting capabilities
- ๐ค Offer recommended actions based on data analysis
- ๐ฏ Generate risk scores on future positions to aid decision-making
Improved Payment Services:
- ๐ค Automate payment tracking and reconciliation processes
- โก๏ธ Enhance efficiency and accuracy of payment-related services
Central Dashboard:
- ๐ป Real-time consolidated data from multiple banks into a single dashboard
- ๐ฐ Real-time cash forecasting and balances
- ๐ก๏ธ Fraud protection
- ๐ Custom reporting for internal use
- ๐ณ Receivables reconciliation
- ๐ SO20022 compliance
- ๐ Data analytics (risk scores on positions)
Central Dashboard with Map Data:
- ๐ Latitude and longitude coordinates
- ๐ Text with names of geographical areas (countries, states)
- ๐บ๏ธ Choropleth: predefined shapes for geographical areas
- ๐ Scatter: data markers to indicate data points
- ๐ฅ Heatmap: color intensity of data points
More MongoDB Advantages:
- ๐ Binary JSON format (BSON): faster parsing, searched and indexed
- โก๏ธ Increasing performance for ad-hoc queries
- ๐ Significant difference at scale for field queries, range queries, regular expression searches
- ๐พ 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