01 |Active Business Learning

Automate price optimization with customer journey based ML pipeline

B2Metric automates the risk management process for a minimum loss ratio with great modeling of 100% explainability. B2Metric can not only turn your customer data into business insights but can use that information to make intelligent decisions, predict outcomes, suggest actions and automate tasks based on machine learning. B2Metric AI automates your machine learning approach with a disruptive way. AutoML approach automates the ML modeling and feature engineering steps with highest increase on accuracy of prediction scores. This makes your customers behaviour much more understanable by your marketing and underwriting team.

Price Optimization with Active Business Learning

B2Metric main vision is building continuous business learning infrastructure for the underwriting process of the financial services and insurances.

We create an innovative prescriptive analytics platform with the power of AutoML. Machine learning significantly makes better resolution making through sophisticated predictive analytics that learns patterns from historical data. Under the superintendence of data scientists, predictive analytics enables organizations to convert their business through these enterprises. Nevertheless, data scientists have not adequate sources and this lack prevents businesses from without exception understand the potential to obtain merit from their data.


Machine Learning Studio


B2Metric Machine Learning Studio is an ML Pipeline solution for the customer journey analytics which works cloud-native solution. BMS is a platform that you can manage and develop your Machine Learning models. Enables to integrate Auto ML to your CRM Systems and your software development life cycle. Automate the fuss of getting your data Machine Learning ready through automated generation of advanced ML pipelines,

Data Gathering & Cleaning

Data cleaning has been a long-unsolved challenge that has plagued the data science and analytics industry. Data scientists spend numerous hours preparing their data for modeling.

Data cleaning is the step to be consistent with the customer database by identifying and removing incorrect data. The most important purpose of Data Cleaning is to recognize and receive mistakes and dual data, unstructured data, planning analysis, your brand’s goals and decisions in the texts, forcing you to export, store and organize typical menus that you cannot collect. Examine unstructured data analysis and tools. This makes better the quality of the exercise data for analytics and It makes the right decision. Most of the time, you should keep unstructured data in Word document databases and manually analyze the analysis tools in databases to prove this data.

B2Metric AI brings data preparation feature automatically for data scientists. Together with this developing technology, it has taken the new developments and technology data gathering & cleaning process to an advanced and higher-level providing users a new perspective and high level of experience.

Different types of data will require different types of cleaning. However, the systematic approach laid out in this lesson can always serve as a good starting point with using B2Metric AI. Data scientists and business analysts achieve this with a click of a button.

Furthermore, data cleaning is one of those things that everyone does but no one really talks about it. Surely, it’s not the best part of machine learning. And no, there aren’t hidden tricks and secrets to uncover. 

Data gathering is one of the most important processes in solving any examine ML problems. To establish a successful machine learning model, an organization must have the ability to train, test, and verify them before starting production. Data preparation technology is used to create a clean and explanatory basis for today’s modern machine learning, but good DP historically takes more time than other parts of the machine learning process.

Most machine-learning algorithms require that data be formatted in a very particular way, so data sets often require some preparation before providing useful information. Some data sets contain values that are missing, invalid, or otherwise difficult for an algorithm to handle. If the data is missing, the algorithm cannot use it. If the data is void, it causes the algorithm to procreate less accurate and even elusive results. Good data arrangement produces cleaner and better-curated data leading to more practical, accurate model results.

Data collection allows you to keep a record of past events so we can use data analysis to find duplicate patterns. From these patterns, you create prescience models using machine learning algorithms.

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Automated Feature Engineering

A characteristic or a set of characteristics can be considered as a feature in the act of machine learning. When these characteristics are transformed into some measurable form, they are labeled as features. Feature engineering is creating new input features from your existing ones. In general, the data cleaning process can be assumed as a subtraction process and the feature engineering as a process of addition. Feature engineering can directly be defined as the process of creating new features from the existing features in a dataset. 

Building machine learning models can often be a complex and boring process and involves many steps. So, B2Metric ML Studio is able to automate a certain percentage of feature engineering tasks, then the data scientists or the domain experts can focus on other aspects of the model. 

Automated feature engineering helps on time saving, building better predictive models, creating meaningful features and preventing leakage of datas. At the same time, the datas are prepared by B2Metric AI to automatically modeling, to execute operations like one-hot encoding, missing data imputation, text mining, standardization and data partitioning.

If this has to be done by hand this would have taken several days, yet with automated machine learning, this only took hardly any hours.The key strength of the automated feature engineering is when it’s applied to regrouping or reshaping data. This is why it is recommended that engaging the creativity and experience of business domain experts for domain-specific feature knowledge, such as how to correctly interpret the data.

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Automated Modeling

The process of automating the wasteful time, iterative jobs of machine learning model development is called automated machine learning, also referred to as Auto-ML. It permits analysts, data scientists, and developers to build ML models with high scale, efficiency, and productivity all while sustaining model quality. B2Metric AI main technology got the basis of Auto-ML. Automated machine learning is the works of automating the end-to-end process of applying machine learning to complex real-world problems. B2Metric AutoML makes machine learning available in the right sense, even to people with no major expertise in this field.

Auto-ML takes advantage of the strengths of both humans and computers. Humans are proficient at communication, engagement, context and knowledge, as well as creativity and insight. Software systems and computers are excellent for repeated tasks, mathematics, data manipulation and parallel processing. Also, they provide humans to achieve master complex solutions.

The traditional way of ML model development process is a resource and labor-intensive, requiring critical domain expertise and time to produce and compare dozens of models. Apply automated ML when you want B2Metric Machine Learning to train and tune a model for you using the target metric you specify. 

Manually finding the right algorithm and tuning it to fit your dataset, well is a challenging task. B2Metric AI technology automates the algorithm selection and hyperparameter optimization on algorithms ranging from classical scikit-learn algorithms to complex time series algorithms. Every model built-in B2Metric AI can be put into production straight away. You can upload data to be scored in bundles. Monitor the performance of all deployed models from a central portal, and easily refresh and replace models if data and accuracy changes over time.

Automated ML replaces much of the work that is done by hand required by a more traditional data science process. But if it is wanted to be considered as a fully automated machine learning solution, a platform must meet these key points: Preparing Data, Feature Engineering, Diverse Algorithms, Algorithm Selection, Training and Tuning, Ensembling, Head-to-Head Model Competitions, Human-Friendly Insights, Easy Deployment, Model Monitoring and Management.

The success of machine learning in various applications has led to an ever-increasing demand for ML systems that can be used off the shelf by non-specialists. It leans to automate the maximum number of steps in an ML pipeline with a minimum size of human effort and without compromising the model’s performance.

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Quick Deployment & API Support

Model deployments on the data stream and the predictions are live and on the spot. You can deploy on a dynamic B2Metric AI enables you to watch real-time updates from dashboards. If no ready-made AI service are available off-the-rack, that does not mean you have to build everything from the ground up with libraries. There is a middle ground: customizable AI and ML models that you can train with your own data. In this way, you can save more time and money. Thus, you can make and expect a faster and as well healthier decision than the feedback you need. As a result, you can integrate it into your own product using APIs. 

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Hunter | Price Optimizer & Risk Manager

B2Metric is an AI - based intelligent software that enabling underwriters to optimize their underwriting processes with AI. While a report by McKinsey discusses “The impact of AI on future of insurance.” AI can deliver on insurance industry expectations through machine learning and deep learning.

1 | Automate the price optimization so you can focus on great modeling with 100% explainability.

2 | Models can combine thousands of variables for deeper insights. It works on-premise or in the cloud.

3 | Customers see a 20% increase in approval rates without risk.

4 | Focuses claim handler and investigator time on claims where they are most needed

5 | Cost - effective; rapid path to ROI

Underwriting Price Optimization Process Optimization

The insurance industry is in the midst of a radical, digitally infused shake-up. Customers are embracing digital channels, and technologies such as the connected car, smart solutions, and artificial intelligence (AI) has ushered in an era of new products built on analytics. B2Metric AI improves your customer experience during the claims process and rapidly detects insurance fraud in claims with deep learning technology. AI-powered anti-fraud software helps you increase your direct fraud savings over 2x.

1 | Platform Independent

Force uses client claims and policy premium data in any format

2 | AI Analysis

With contextual guidance to power investigations

3 | Alerts & Notifications

Sends real-time alerts or batched notifications for suspicious cases to fraud handlers

4 | 80% Hit Rate

Force gives more accurate information with fewer false positives than any solutions in the market

03 |Use Cases by Industries


To be profitable in the insurance industry, you must avoid being adversely selected against. B2Metric AI helps your insurance process to avoid this and maintain your underwriting margins. Claim processing is the center of the insurance business. Even tiny improvements that reduce processing time and increase accuracy can drive huge impacts.

Finance Services

Financial Services firms recognize how AI based advanced analytics can reduce costs, improve operations, and increase customer satisfaction. Reshape your company from internal operations to customer experience to treasury services and payments.

04 |Clients
Aksigorta & B2Metric
Allianz Insurance & B2Metric & B2Metric
Otokoç & B2Metric
Turkcell Sigorta & B2Metric
05 |Register Now

Feel free to contact with us to try B2Metric's AI powered product and services.