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What is it and why is it interesting for your business?

Ensuring accurate treasury forecasts is crucial for any business, as it directly impacts financial planning, liquidity management, and risk mitigation strategies.

By accurately predicting cash flows, a company can optimize its capital allocation, manage working capital effectively, and make informed investment decisions. Additionally, reliable treasury forecasts enable proactive management of financial risks such as liquidity shortages, interest rate fluctuations, and currency volatility, reducing the likelihood of financial crises or disruptions to business operations. Furthermore, it optimises collection management to prioritize customers with highest likelihood and importance to be contacted.

How does it work?

In our approach, we utilize both regression and classification algorithms to generate predictions for treasury forecasting. With regression algorithms, we predict a numeric value, while classification algorithms allow us to predict multiple classes with associated likelihoods. Given these options, we opted for multiple classification predictions with predefined buckets, offering a broader understanding of potential delays and their severity.

• Step 1: predicting if an invoice will be paid on time, yielding a binary outcome of "Yes" or "No."
• Step 2: if the invoice is predicted to be unpaid, we predict whether it will be settled before the end of the month, recognizing the significance of month-end, quarter-end, and year-end deadlines in finance.

By leveraging these forecasts, our customer can optimize cash pooling strategies, ensuring efficient allocation of funds across accounts and minimizing idle cash balances. Additionally, they can strategically request loans in specific currencies based on predicted cash flows, mitigating currency risks and optimizing borrowing costs. Lastly this provide prioritization to the collections team within their contact strategy by defining a prioritized call list for each customer.

What is it and why is it interesting for your business?

In contract fulfillment, discrepancies between specified volume commitments and actual order quantities can occur due to various factors, including end-product customer demand or alternative supply sources. Sales departments are committed to achieving higher contract fulfillment rates to meet client expectations and maximize revenue.

How does it work?

To address this challenge, we set up a detection model that automatically sends alerts directed towards account managers. These alerts serve two primary purposes:

• To prompt account managers to encourage customers to proceed with purchasing the goods despite deviations from the contract terms
• To facilitate the selling of excess goods to alternative customers.

By implementing these automated alerts, we streamline the process of managing contract discrepancies, optimize inventory utilization, and ultimately enhance overall contract fulfillment performance. This proactive approach ensures that we not only meet our contractual obligations but also capitalize on opportunities to drive additional revenue and strengthen customer relationships.

What is it and why is it interesting for your business?

Employability scoring for recruitment involves the use of machine learning algorithms to predict the likelihood of job candidates being successfully matched with available positions.

At element61, we recognize the importance of streamlining the recruitment process to efficiently identify the most promising candidates in the HR and interim business. Drawing from a past use case with Vivaldis, a Belgian interim office, we understand the challenges of processing a high volume of job candidates without a prioritization system in place. By implementing an employability scoring model, we were able to rank candidates based on their likelihood of finding a job, allowing recruiters to focus their efforts on reviewing the most relevant resumes first.

How does it work?

In order to scan the most relevant resumes, we have built a machine learning model which predicts which candidates are most likely to be matched with a job. The employees can then scan through the most relevant resumes and as such, tackle high value candidates as fast as possible. At this point, the machine learning model is not scoring the candidates for a specific job but rather the overall employability of a candidate.

In order to define a score, we consider different drivers for the employability score such as age, language knowledge, education level, driving permit, experience, etc. Important when working with sensitive data, is that the model is built to avoid any discrimination or bias. Among others, this means we don’t include certain drivers like gender & region. The tooling developed is working hand-in-hand with Salesforce, the customer tool in place to collect and store all the information about the candidates (both information received in the offices as on the website itself). This means that Salesforce remains the end-user tool & embeds our solution.