"Cashflow is the lifeblood of all businesses. The accuracy of cashflow forecasting is critical to the identification of potential shortfalls in cash balances, ensuring that payments can be made to both staff and suppliers, enabling growth including access to finance and ultimately avoiding insolvency.

Despite trends towards improving cashflow management practices in the UK, the inability to manage cashflow effectively is still a significant cause of small business failure (50% of SMEs fail in their first five years of operation with 80% of failures due to cashflow issues), with \\\>42% of SME employers citing cashflow as one the biggest obstacles facing their business (BEIS, 2015). Key reasons for cashflow challenges include increased debtor days, inadequate credit control, late invoicing, and even poor accounting practices. The UK is not alone with a recent study of 1.8M SMEs across the EU and US identifying common challenges.

Despite being the foundation upon which major financial decisions are made, cashflow forecasting is a largely manual process based on historic P&L/sales data, averages and often flawed assumptions. With a greater level of transactions, this becomes even more challenging, with the use of existing modelling software offering only a marginal improvement compared to Excel.

Fluidly (a UK based SME) seeks to address the market gap for more effective forecasting solutions through the development of the first automated web based cashflow forecasting application that uniquely applies data science and machine learning to cloud accounting data (offering a level of granularity not currently possible, forecasting each nominal account line with a cash association), offering improved debtor management and prediction accuracy.

If successful, this disruptive solution will offer:

* Improved accuracy in cashflow forecasting to enable UK companies (initially aimed at SMEs, accountants) to track, manage, pre-empt cashflow issues for better financial control and enabling more informed financial decisions to be made.
* Accurate line by line forecasting in near real time - granularity currently not possible by human/manual analysis (predicting the likelihood of a payment being made to the day).
* Compatibility across any accounting package.
* Anomaly detection (threat/opportunities including fraudulent activity).
* Supporting start-ups to survive and scale through improved use of working capital, potentially reducing the need for external finance, or where needed aiding lending decisions.

The solution has global exploitation potential and offers future potential in logistical planning and energy demand/supply forecasting applications."