Nowadays, almost all banks are struggling with big data stacks. Although most traditional methods are used to solve this struggle, there is a significant loss of time and resources. However, machine learning provides an end-to-end solution and simplifies the process. The banking sector is listed by analysts as one of the sectors that will benefit the most from machine learning. Until 2030, it is expected to save 1 trillion dollars in banking sector by using only machine learning solutions. It provides solutions far beyond rule-based software, especially in the detection of credit card frauds, credit risk scoring and payment predictions.

In the real estate market, it is not only difficult to invest at the right time, but it is also difficult to value a property. There is no doubt that a property to be bought or rented, both the landlord and the buyer / tenant wants to know the value. But it is often valued by using traditional method which is looking at similar properties. However, through scoring, all the data of both property and the similar ones are processed retrospectively and can develop both a value for today and a forecast for the future. Unlike traditional methods, our scoring engine allows to obtain a certain price. It can score the future value of a property and determination of the value of a property to be sold or rented.

When it comes to customer service, artificial intelligence blends knowledge and automation that provides unprecedented levels of customer insight, and further enhances the service process and quality. This level of service leads consumers to gain brand loyalty. With AnalytiXR, OptiWisdom can help your company to scale and improve customer relationship management quickly. AnalytiXR uses the latest scoring algorithms to effectively measure customer satisfaction, the most valuable customer, customer churn and gain. It provides insight into how to turn visitors into advocator with scoring customer satisfaction for each level of customer through the funnel built into the product.

Machine learning has become a real game changer in human resources. Their main use is to evaluate the performance of employees by removing them from the subjectivity and to identify the employees closest to the resignation or in need of training. It is also used to perform the division of labor appropriate to the qualifications of the employees.Companies should hire the right candidates for the job and ensure that their valuable employees stay in the company for many years. The adoption of artificial intelligence in human resources will accelerate the recruitment process and follow the job satisfaction of the employees. This is exactly what our AnalytiXR product is designed for. By measuring employee satisfaction, turnover rate can be kept at an optimum level, objective employee performance evaluations can be made and measures can be taken for the position of employees close to resignation.

It is possible for telecom companies to improve their customer experience with data from devices, networks, smartphone applications, geolocation information, and customer profile details. However, the analysis of such large-scale data leads to a more complex process. On the other hand, by means of artificial intelligence and machine learning, big data will be analyzed and utilizable information will be obtained, customer experiences will be improved and customer satisfaction will increase, thus company revenues will increase. Gartner predicts that by 2020, 20.4 billion devices will be in use worldwide and the need for artificial intelligence applications in the telecommunications industry will increase. Based on these informations, we aim to enable telecom companies to score, segment their customers and match the right customer with the best offers and services with our engines.