Here are some examples of how AI-based solutions can be implemented and can help SMEs to develop their business:
- Working capital analysis to understand and predict gaps in liquidity and finance them
- Support efficiency decisions. For example, this decision can be supported by the pivot of cash flow analysis and market insights.
- Support different strategies. For example, increase pricing considering the seasonality effects or requiring patterns.
- Environmental impact.
What banks should remember and pay attention to:
- Customer behavior is changing. 70% of the entrepreneurs are Millennials now with increasing digital banking needs.
- Usage of the digital channels is increasing
- Multi banking. Business customers tend now to have more accounts than retail customers to have access to credits and manage the liquidity.
All that changes the way customers consume banking products moving toward modern digital cloud solutions, and new service models.
How to use data to get more customer insights:
- Insight-based offering. Unleashing event-based marketing actions towards own or third-party offers
- AI. Apply machine learning logic
- Data enrichment. Raw financial data analysis to extract information
- Aggregation. Third-party data accounts aggregation.
And the use case of that is BFM (Business Financial Management) solutions, that can be embedded into online banking for SMEs, for example, and/or e-invoicing platform, and help SMEs to analyze, visualize financial data, predict cash flow and plan how to finance possible financial gaps.
Watch the full presentation to get more details: