Machine Learning for companies

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Added Value through Artificial Intelligence

Groundbreaking technological and scientific advances and the growing availability of meaningful data are leading to an increasingly profitable and frequent use of artificial intelligence in the corporate context. Existing business processes and products, for example, can be optimized and made more efficient through the use of machine mearning and artificial intelligence. At the same time, novel business models are made possible that hold out the prospect of new earnings potential.

Artificial Intelligence (AI)

The term "artificial intelligence" is a generic term for all methods that attempt to imitate partial aspects of human intelligence by imitation. In this context learning ability, perception, logical decision making and pattern recognition play a significant role. For example systems can recognize animals on pictures without knowing what an animal is, how it lives, moves etc.. But if a task goes beyond the learned special discipline, artificial intelligence fails. Rule-based "expert systems", such as chess computers, can also be understood as a form of artificial intelligence.

Machine Learning (ML)

"Machine learning" refers to a procedure in which artificial intelligence is represented by a computer system that can learn complex patterns and structures from data. Statistical models and algorithms are able to learn a relationship between input and output data without human intervention and also transfer this relationship to new data. The dynamic algorithm of a machine learning model tries to optimize a user defined target function. This distinguishes machine learning models from classical decision systems with rigid decision rules.

Deep Learning (DL)

Deep learning is a much considered sub-area of machine learning, which is inspired by the structure and functioning of the human brain. In deep learning, modelled neural networks process information. Data is fed into the network as input and in deeper layers of the network (deep) more and more complex structures can be learned (learning) to produce certain results. Such neural networks are able to learn from historical data fed by modern algorithms. Based on the output, forecasts, decisions or patterns can be derived.

Forecasts and predictive analytics for decision-making processes

Machine learning algorithms are able to recognize relationships and trends in huge amounts of data and from this derive forecasts and recommendations for important sales and cost-relevant business decisions. In this way, the company's profit can be directly increased by AI.

Machine Learning use cases for companies

Due to increasing market developments and growing competitive pressure, machine learning use cases in companies are growing

Success factors and pitfalls in a Machine Learning Project

Are you planning a machine learning project? We tell you how you can implement it successfully and what you should avoid.

Open customer invoices are often not paid on the due date. With machine learning, late or premature payments can be better predicted, enabling intelligent liquidity and cash flow planning.

AI in Finance Cash-In Prediction

AI makes it possible to predict the fluctuation probability of an employee. In this way, better decisions can be made in HR and the recruitment needs of different company divisions can be better planned.

AI in HR employee fluctuation

In procurement, machine learning helps to calculate the actual total costs from the planned material, personnel and other costs. Possible cost increases can be identified and suitable countermeasures taken.

AI in Procurement Estimate demand and costs

On the basis of previous leads and open negotiations, the actual turnover can be predicted.

AI in Sales Revenue Forecast

Planning with Machine Learning - Cash-In Prediction

A reliable forecast of incoming payments is crucial for the planning of cash and cash equivalents. If it is to be predicted when invoices will be paid, a complex framework of internal and external factors plays a role, which are optimally evaluated with machine learning. Calendar-based information such as weekdays and public holidays, key figures about the payment behavior of a debtor and current global conditions are obtained from internal and external data sources. Daily forecasts are then automatically generated and forwarded to the responsible parties in the form of reports. There, the information helps to estimate the short-term capital requirement or surplus.

Automation with Machine Learning - Automated Invoice Processing

Automated invoice processing must be able to handle both paper invoices and invoices in digital formats (email, PDF). For this purpose, optical character recognition (OCR) is used to process scanned and photographed documents. Data extraction and validation provides the required text elements, which are transferred to a database. A subsequent classification of the invoices and conditional further processing can also be initiated by machine learning. This process saves time, which results in reduced processing costs.

Detecting Fraud with Machine Learning - Fraud Detection

Digital business processes in particular are exposed to an increased risk of attack and fraud. machine learning supported fraud analysis can help to increase security not only in purchasing. Instead of processing all types of fraud individually, the system issues a warning in case of behavioral deviations of customers or irregularities in transactions. The particular challenge is to create a system that reliably detects fraud without triggering an unnecessary flood of warnings that would rather take the strain off the employees than relieve them.

Optimize with Machine Learning - Customer Segmentation

Target group-oriented or behavior-based customer segmentation - there are many ways to boost marketing and sales. With a customer segmentation, those customers are identified who will stimulate your business. In addition to the profitable ones, there are also customers who are comparatively unprofitable or not profitable at all due to low sales. In both cases, customer acquisition and retention causes costs through discounts, support and marketing campaigns. Invest your capital optimally in the customers with the highest profitability. With machine learning and the RFM (Recency, Frequency, Monetary) classification approach, an individual indicator per customer can be created based on sales and order interval, among other things, which can optimize further sales activities. In this context, the data-driven detection of customer churn can also be implemented.