B2Metric AutoML Insights

B2Metric brings you the latest in enterprise machine learning, artificial intelligence, price optimization and risk management, customer journey analytics news. We believe in a world where everyone has access to fair and transparent credit, and machine learning is the way to get us there. Machine learning is changing every industry, and now’s the time to bring this new predictive power to your lending business.

B2Metric Blog Post
Anomaly Detection with Machine Learning

Anomaly detection is a method to notice anomalous actions or data. It predominantly focuses on the problem referring to recognize all intrusive attacks based on their anomalous activities that diverge from “normal activity profile” in a system. It also checks to see if an activity is within the predetermined acceptable condition. If it is not in this scope, it assumes that it is known as an unacceptable or bad attitude. The common way of the anomaly detection method occurs from the following components: First, a basis for all acceptable behaviors and situations is created. All activities observed while establishing this basis are taken as basis. The current situation is configured. This configuration is for a model that can be interpreted by applicable technologies. The observed activity is compared with the basis model that created, it is checked whether there is a deviation. Situations that observed as an anomaly are reported. Anomaly detection, rare observations or situations that are disparate from the remainder of the watchings, which may cause suspicion. Such "abnormal" situations typically transform into a kind of problem, such as a fault machine on a server, cyber attack, failure capsules in the cloud network, financial frauds, mobile sensor data, statistical process control (SPC) for production.
The best anomaly detection frame:
1-) Estimate main errors with up to 95% accuracy,
2-) Notice uncommon changes in system actions spontaneously,
3-) Service providers should strictly know how to fix issues. Therefore, show elementary to understand root cause analysis.

Img. reference site

Challenges in Anomaly Detection Models

Some difficulties make the task of anomaly detection difficult. Machine learning algorithms often need large amounts of data. This is because anomalies are not very likely, they are statistically small, and data sets are often unstable. Train and test data of models that developed for detect anomalies may be finite. It may also be unlabeled for testing and training. For example, there are states that normal behavior is more than abnormal behavior. This causes additional difficulties in training models that detect and predict abnormalities. Anomaly detection system ought to be as a dynamic system with fast-growing usage bases. In addition, as the underlying system develops, it has to update its behavior over time and adapt it to development.

How Anomaly Detection Approach Works?

Technically, the most distinctive criteria between normal and abnormal data point is whether there are similar data points around it in the analytical plane. In this context, the areas where similar points become very clustered are considered normal, and the areas where they become sparse are called abnormal areas. This is where the inference benefits of machine learning algorithms on analytical planes come into play. After these regions are determined by machine learning algorithms, abnormalities of data points are predicted. Unbalanced data visualization that taken from Towards DataScience



Many machine learning algorithms have been developed throughout the history of machine learning to identify these areas. So what makes machine learning algorithms different for abnormal detection processes? There are 2 major detection ways to process abnormal patterns; supervised and unsupervised anomaly detection.

Supervised Anomaly Detection:

The tagged dataset that includes both abnormal and normal sample data to create a prediction model that can classify future data points is needed for the supervised anomaly detection method. Algorithms such as Support Vector Machine Learning, Supervised Neural Networks, K-Nearest Neighbors Classifier are frequently used algorithms for this motive.



Unsupervised Anomaly Detection:

In this method, any training data does not necessary. Unsupervised anomaly detection assumes two things about data rather than training data. Only a percentage of the data is abnormal and any anomaly is completely dissimilar from normal samples. After these surmises, the data is clustered using the measure of similarity, and then data points that away from the cluster are appraised as anomalies. Large labeled data sets are needed to train these algorithms and achieve high-performance estimation results. Conversely, it is difficult to obtain such large-scale tagged data sets, and field knowledge from professional is necessary for the disclosure process.

The thriving performance of supervised learning in previous years has also led to unsupervised learning achieving very good results. Although there is a new tendency to adopt unsupervised attempts, attempts based on ML algorithms on anomaly detection generally focus on supervised models. The scarcity of tagged data is increasingly seeking to develop unsupervised learning models. B2Metric Machine Learning Studio (Register & Start Free Trial Now!) can be applied to these and many other problems, it solves these problems for you and allows you to make anomaly determinations in the most accurate way.

Real World Scenario of Where Anomaly Detection Used?

Anomaly detection affects business decisions across sectors. Sectors such of, insurance, finance, telecom, manufacturing, banking are the main sectors which anomaly detection is of great importance. Detection and prevention of abnormally high purchases-deposits, fraudulent spends, revenue fraud, abuse, service disruptions are main real case scenarios of anomaly detection.



Insurance Frauds

According to FBI reports; there is $40B loss for Insurance frauds in United States every year.

Anomaly detection in the insurance sector is one of the services that takes basic problems in different fields of insurance. For instance, identification of fraud in insurance and securities, and irregularity detection in health services' data are among the scopes of anomaly detection in insurance. In addition to these services, increasing cybersecurity calls have become a need in the insurance industry in recent years. With these developments, damage fraud detection actions have started in the insurance industry. In short, anomaly detection is a method used for insurance fraud detection. For instance, insurance companies can use anomaly detection technology to identify suspicious user behavior in the insurers' network.

Anomaly Detection for Telecommunication Industry

With the development of the telecommunications sector, the sector started to produce and collect huge amounts of data. These data are so large that it is impossible to deal with this data manually. Therefore, data mining technologies for the telecommunication sector develop. Abnormal situations such as network failures occurring in telecoms and unusual customer calls are called anomalies. Detection of these anomalies has an important place in the telecom sector.

Cyber Security Anomaly Detection

Network monitoring tools owned by cyber security systems can learn normal network behavior due to the large amount of data they have. Entries that unusual and intrusion are called anomalies. These anomalies must be detected and intervened to ensure cyber security. Denial of service (DoS) attacks is an example of anomaly. Although they don't crash or receive data, DoS attackers aim and focus on downloading a network and rejecting service to legitimate users. Starting DoS attacks is easy. DoS attacks block users from getting the right service by forcing physical resources or network connections. The attack happens the service is filled with too much traffic or data. Therefore, DoS attacks must be detected. For this, first of all, normal behaviors should be specified in the system. Then the system should alarm when the behavior deviates from normal to anomaly (DoS).

Network Faults Anomaly Detection

In order to provide high quality service in IP networks, the downtime of the service should be shortened as much as possible after the network errors occur. However, there are also network errors that cannot be detected by operators by simply monitoring device states. It is necessary to focus on anomaly detection to solve such abnormal problems.

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Customer Journey Analysis with Machine Learning

Customer Journey Analysis is an approach to help a company see its products and services from a customer's perspective. Before the analysis, what the customer journey is and its importance should be examined. Do you know what is it and why you need it?



What is Customer Journey?

Customer journey cites to the path followed through the points of contact of your customers and potential customers before making a purchase action. Customer Journeys explains the path to successive interactions a customer has with a product, service, and company. A customer journey is an observation way about understanding your users, how they behave while they visit your website, and what you can do to improve their trip. They keep coming because of this observation.

Customer Journey Analytics

Marketing is the main area of ​​use for customer analysis. There have been major changes in customer behavior in recent years. Customers usually do not decide to buy a product at the first interaction now years. They often examine different brands several times for a product or service before making a decision. Continuously developing mobile technologies enabled customers to interact with organizations from many different channels. On the Internet, the points of contact of potential customers for a product or service are hard can be watched from multiple channels. With the customer journey, you can better analyze, make sense of these changes in customers' behaviors, and use them to create your marketing strategies. Because customer journey proceeds at these touchpoints.



Why Do You Need Customer Journey Analytics As a Business Owner?

Customer journey analysis allows you to identify your customer touchpoints. Today, companies should think like their customers; customer journey analysis makes this easy. Starting to think like your customers and evaluating your products/services like them will increase your sales. Analyzing customer journeys reveal inconsistencies. This allows us to detect discrepancies and corrects. Analysis of journeys and acting on what is learned can reduce the effort needed of customers, rising their satisfaction and decreasing the number of abandoned journeys. Analyzing a customer's travel trends helps service providers find better ways. Analyzing journeys may push a dialog between departments to develop entire effectiveness, overcoming departmental sub-optimization. By creating and analyzing multiple customer journeys, you can provide test scenarios for a multi-channel solution. Besides, these analyzes and inferences can also be used for your other customers. Thus, your efficiency increases while your operation cost decreases.

Customer Journey



Benefits of Customer Journey Mapping

With customer journey analysis, you can easily see the points where your customers interact with your business Customer journey analysis helps you in your sales process by providing you with an outside perspective. Customer journey analysis helps you to focus your business on customer needs It allows you to compare targeted and acquired customer experiences.

How Machine Learning Improves Customer Analysis?

In the 20th century, with the business now turning around the customer, companies move away from traditional business models and turn to customer-oriented and personalized business models. The customer experience has become the priority way of companies to set themselves apart from their competitors. Customer journey analysis is one of the biggest providers of customer-oriented personalization. Making customer journey analysis with machine learning allows you to analyze your customers and create your business plans much more easily than other methods. With machine learning and artificial intelligence algorithms, you can easily analyze your customer data and draw meaningful results from them. Such as customer churn prediction. In line with these results, you can create a business plan that makes a difference in many business areas, especially marketing and sales areas.

Customer Journey

B2Metric Machine Learning Studio (Register & Start Free Trial Now!) uses different types of machine learning algorithms in autoML pipeline to provide the best solution for you. Each algorithm that B2Metric provides for you can be adjusted to get the most suitable model and provides you the best customer experience. You can see the customer churn segmentation screen created with B2Metric Machine Learning Studio (BMS) above.

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Customer Journey

How Can You Analyse a Customer Journey Map?

Journey maps are infographic visualizations to understand how a customer is working towards a goal over time. There are several ways for analyze customer journey maps.

1) Identify points where customer expectations are not met. When users interact, if this interaction does not meet expectations, pain points are seen on the journey. In these cases, it is necessary to think from a user perspective. So you can understand which interactions did not meet the old expectations and experiences.

2) Identify any unnecessary touchpoints. Whether to take steps that can be eliminated to facilitate the overall experience should be examined. You should look for ways to reduce the cost of interaction.

3) Look for low points. You should see where the lowest points of the journey come from. Determine which low points you should prioritize.

4) Bestow time periods for the main stages of the journey on your travel map. Consider how long it took users to reach the sub-steps and whether these elapsed times were appropriate.

5) Look for moments of truth. Some points are based on some other points on the journey. These points are very important for the journey. If these moments go well, they can save the journey. You should analyze these points well and make sure you pay attention to them.

6) Identify the points that expectations meet. Look where the customer journeys are highest. These points are usually interactions that users are happy with. You can use similar experiences in different places. Thus, experiences will increased by you.

7) Determine high-friction channel transitions. There are many trips between the channels. The journey is disturbed when users change channels and transition should be facilitated.

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How the COVID-19 Affects the Insurance Industry?

The World Health Organization (WHO) was informed of an outbreak of “pneumonia of unknown cause” detected in Wuhan City, Hubei Province, China – the seventh-largest city in China with 11 million residents on December 31, 2019. As of January 23, there are over 800 cases of 2019-nCoV confirmed globally, including cases in at least 20 regions in China and nine countries/territories. Later on, Chinese authorities reported that laboratory tests excluded SARS-CoV, MERS-CoV, influenza, avian influenza, adenovirus, and other common respiratory pathogens. Coronaviruses are known to be a large family of viruses, some of which cause less serious diseases like the common cold and others more serious diseases like MERS and SARS. However, some of them are easily transmitted from person to person, while others do not transmit.

Covid Visualizer

Corona Virus in Our Lives

Covid-19, commonly known as coronavirus, is a serious global infectious disease outbreak with more than 400,000 cases and over 25,000 deaths worldwide up to 28 March. You can check the numbers whenever you want from the Covid-visualizer. The coronavirus has a huge impact on almost every aspect of our daily life. Everything from our social habits to our working order has recently changed with this virus. These changes not only happened in our personal life but also affected all sectors. The insurance sector is also one of the sectors affected by the virus.

Coronavirus effects on Insurers

Coronavirus Effects on Insurers

The Coronavirus Disease (COVID-19) outbreak will likely influence life insurers in the following ways: Increment incurred claim costs, including death and disability claims, and drug prices. Adverse actions in the financial markets, covering declines in bond yields, equity markets, and real estate, decreasing profitability. Business deduction and potential influence on profitability. As coronavirus continues to extend across to the whole world, its effects are rippling across financial markets and the global economy. The life insurance industry is no exclusion, particularly given the incrementally global nature of many insurers and their biggish investment portfolios. Although most countries have had minimal reported coronavirus cases so far, and as a result, the efficacy on claim costs for life insurers is remissible to now, the effect on business operations for some global insurers has already emerged, particularly for those operating in high-risk regions such as US, China, Iran, and Italy. Most life insurers await to feel the effect of coronavirus on the financial markets because of the highly interlinked global economy. Officials said the coronavirus has changed customers' behavior, and insurance companies should take this into account. According to Christopher Woolard who is the interim chief executive of the FCA,(Financial Conduct Authority) insurance firms should realize this and treat their customers fairly, realizing the conditions customers may find themselves in. Epidemics are eliminated from many business insurance policies, therefore the early prognosis was for low levels of claims. But as stagnation intimidations, the global economy along with incrementing insolvencies, all sorts of companies with trade credit insurance are coming under strain. Meanwhile, insurers’ investments are also coming under strain.

Coronavirus

How Pandemic Effects on Car Insurance

Obviously the coronavirus will deeply affect the health insurance and also car insurance industry especially car purchases and loans affected by this pandemic. The ability to pay car insurance will also be affected by the coronavirus. Therefore some insurance companies are extending the time for their customers who have difficulty paying due to the coronavirus pandemic. According to WHO, almost 1.24 million people die in car crashes worldwide and according to Safer America, the yearly cost of traffic accidents in the United States is estimated to be $871 billion. These rates and numbers are expected to drop due to the virus, even if precise data has not yet been shared. Because, with the virus, driving rates decreased. This caused the accident rates to drop.

Charts of Deaths in US

Above are the weekly death rates in the USA. Here, we see that death rates have decreased because of the reduction of leaving home due to corona. This decrease is expected to be seen in the rate of traffic accidents for the same reason.

Do the Turkish Life Insurers have COVID-19 covered?

There is a great article that posted by KPMG here  Do insurers have COVID-19 covered? 
In Turkish market as well as worldwide through, many insurance companies have taken the necessary measures against the coronavirus and have taken essential steps. Mapfre Sigorta, Bupa Acıbadem Sigorta, Groupama Sigorta, Eureko Sigorta, and Allianz are the insurance companies that announce a good news for Covid-19 patients. Their life insurance cover all of their corona treatment costs without exclusion.

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AI Use Cases For Telecommunication Industry

The use of AI and machine learning is becoming more and more common. With the widespread use of artificial intelligence and machine learning, AI started to be used in every field of business. Telecommunication is the main one of these fields. Because the telecommunication sector is one of the fastest-growing industries and the one that is affected by innovations quickly. The largest telecommunication companies worldwide also closely follow artificial intelligence and ML and develop various artificial intelligence applications. Some common examples of these application uses are:

Customer Propensity Prediction

Customer data is very important in the telecommunications sector. With the use of artificial intelligence, meaningful information is extracted from these data. AI applications evolve for the best customer communication in telecoms and analyze their movements for them. B2Metric AI exists to predict and procure customers' next step and provide the best option for them.

Churn prediction for telecom

Marketing

Telecommunication companies can use data for creating better profiles for customers and allocate a marketing budget. With developed data structure, they are able to collect and store much more data that provide insights into each customer such as demographics, location, devices used, the frequency of purchases, and usage patterns. They also can have a stronger understanding of their customers by using other sources like social media and combining data from coming there.

Fraud Detection for telecom

Fraud Detection

Machine learning algorithms are efficient in detecting fraudulent behavior such as fake profiles, illegal access, theft, and more with anomaly detection of data. The power of technology-driven fraud detection solutions is derived from their ability to process large sets of data. The general rule is the more data is better, which consequently improves the accuracy and effectiveness of the analysis. B2Metric AI uses several data sources, both internal and external, to help identify fraudulent behavior in realtime with its anomaly detection modules.

Fraud Detection for telecom

Customer Churn Prevention

Customer churn means that the loss of an extending customer to an opponent. Churn prediction is highly important for each industries and also for telecoms because winning new customers is a highly costly issue. Retaining existing customers are cheaper than obtaining new customers. Also, older customers, who have no voluntary deductible excess, are generally non-churning customers. Newer ones, do churn more often. Therefore, telecommunication companies notice that to hold on to existing customers is getting more important. They also agree that churn analysis is one of the main data mining application areas. Hence, decreasing customer churn is the main business target of the telecom business. To manage customer churn, churning customers should be identified first. Then these customers must be convinced to stay. B2Metric AI prevents your customer churn rate by %20 to %50 with robust Churn prediction models that make efficient service.

Network Planning Systems

The creation of network planning systems will require evolving integral technologies like question answering, language transition, speech synthesis are some of the evolving techniques of telecommunication fields. Other applications like digital companding, AI-based network planning systems, are some of the evolving techniques of telecommunication fields. AI has found a broad application to exceed the efficiency of the telecommunications substructure.

Fraud Detection for telecom

Handling with Big Data

The telecommunications industry is one of the sectors that have to get over to big data. The fact that customer data is available in a mixture of various sources often poses a problem. Therefore, managing them manually will be time-consuming and will incur additional costs. Telecommunications, like all organizations in the business sector, try to maximize their revenues by reducing operating costs. With AI and ML, the handle of Big Data will be much easier. Therefore revenue will increase. B2Metric can turn your customer data into business insights and can use that information to make intelligent decisions, predict outcomes, suggest actions and automate tasks based on machine learning. For more information about “B2Metric data gathering& cleaning”

Fraud Detection for telecom

The Future for Telecommunications

Many people may not realize that AI is already widely used in daily activities through digital voice assistants. But data science is already a huge part of the telecommunications sector, and as big data tools become more available and complicated, data science, ML will all continue to enlarge in this range with enhanced data.

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B2Metric Underwriting Risk Management

Underwriting is the duration of assessing the risk of insuring a home, car, driver or individual in the case of life insurance or health insurance. Underwriting is also, determine if it's beneficial for the insurance company to take the chance on providing insurance. After determining "risk", the underwriter sets a charge and adjusts the insurance premium that will be charged in exchange for taking on that risk.

What Does an Underwriter Do?

The role of insurance underwriters is to pick who and what the insurance company will insure based on risk evaluation.

  • Tries to find out what the actual risk is according to reviews of specific information.
  • Adjusts what perils the insurance company agrees to insure or what kind of policy coverage
  • Decides the conditions that the insurance company agrees.
  • Tries to find proactive ways to eliminate or decrease the risk of future insurance claims.
  • May tries to insure you when there are some issues and when the issue isn't clear by work out with your agent or broker.
  • Changes or limits to endorsement coverage.

Briefly, underwriting is a critical risk palliation mechanism accepted in the insurance sector.

Insurance underwriting process

What is the meaning of risk?

Underwriting risk means the risk of loss undertaken by the insurer. In insurance, underwriting risk may appear from an erroneous evaluation of the threats related to writing an insurance policy or from factors that uncontrollable. As a result, the insurer's expenses may overrun the premiums that won. 

In the securities sector,  underwriting risk generally comes up if an underwriter overrates demand for an underwritten issue or if market conditions change abruptly. In such cases, the underwriter may be necessary to keep part of the issue in its inventory or sell at a loss.

An insurance agreement guarantees that the damages and losses caused by covered perils will be covered by the insurance company. Creating insurance policies or writing insurance policies is usually the primary source of income for the insurer. The insurer gathers premiums by underwriting policies and invests the revenues to get gain. An insurer’s profitability hang on how well it understands the risks it insures against and how well it can decrease the costs related with managing claims.

How A.I. Change The Future of Underwriting Process ?

Artificial intelligence is a disruptive force in multiple areas, like finance, healthcare, and security. The insurance industry can also benefit from these huge technology seriously. There some different ways to use AI in the insurance industry.  Risk Mitigation, Fraud Prevention and, Better Underwriting are just some examples of them.

According to report by McKinsey ‘Insurance 2030—The impact of AI on the future of insurance’   AI and its associated technologies will have an important impact on all senses of the insurance industry, from distribution to underwriting and pricing to claims. Advanced technologies and data already have a huge effect on distribution and underwriting, with policies being priced, purchased, and bound in near real-time.

How Does B2Metric AI Technology Distrupt the Insurance Industry?

Underwriting Risk Management based on Artificial Intelligence

Multiple models run with cross modeling using B2Metric AutoML to predict risky customers in auto insurance.

  • Filtered customer proposals coming from all agencies and banks to get the best insights on customers.
  • 20% extra profits (comparing GLM and statistical models) annually by de-risking customers’ accident risks.

Claims processing is the center of the insurance business. Even tiny improvements that reduce processing time and increase accuracy can drive huge impacts. Time is a valuable resource for employees and customers. Use B2Metric Hunter to predicting that claims ought to be a foundation on auto-paid characteristics with deep learning. Thus, minimize your the possibility of mistakes.

Fraud Detection and Prevention

The power of technology driven fraud detection solutions is derived from their ability to process large sets of data. The general rule is the more data the better, which consequently improves the accuracy and effectiveness of the analysis. B2Metric AI uses several data sources, both internal and external, to help insurers identify fraudulent behavior.

Risk Mitigation

Risk management is in a doubt on Insurance companies, brokers and other financial services. It is a wrong idea and just preparation for cash flow or financially bad days. The part of credit risks ensures services concentrate on helping management, reporting of the credit risk description. Financial market places depend to develop prediction models nevertheless they consist of the natural risks.

How Does B2Metric AI Change the Future of Underwriting Risk Management : HUNTER

B2METRIC RISK HUNTER

B2Metric automates the risk management process for a minimum loss ratio with great modeling of 100% explainability which improves your own GLM model results by 20%. AI can deliver on insurance industry expectations through machine learning and deep learning. B2Metric Hunter is the AI-native, SaaS and On-Prem based solution that’s powering risk analytics for the leading insurers. It uses multiple data sources and AI, delivered by dedicated account-based Data Science teams to optimize the claim handling period and drive innovation. 

"B2Metric does not analyze only structured data. You can also analyze the unstructured enhanced data."

Unstructured Data Analysis

Insurance data analysis

  • Reading content from document in scanned image/pdf format with OCR + Img. Recognizer.
  • Analyze data with NLP models to identify already defined red flags for risks or fraudulent situations.
  • Use the result of this data for B2Metric Risk model
  • Reported via dashboards for the actuary department.

AI is preparing to change the insurance industry on a large scale with risk analysis and other developing technologies. Using these technologies, insurance agents will be able to obtain customer insights, behaviors, and participation information that they have never seen and are unsure of. As a result, it will increase the value of buyers, attract new buyers to increase revenue as much as possible by moving your product or service beyond the promises.

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Customer Churn Prediction

Customer churn means that the loss of an extending customer to an opponent. Churn prediction is a highly important job for insurances and finance services because winning new customers is a highly costly issue so keep your current customer base. All costs of customer churn cover both lost income and the marketing costs related to switching those old customers with new customers. Retain existing customers is cheaper than obtaining new customers. Also, older customers, who have no voluntary deductible excess, are generally non-churning customers. Newer ones, do churn more often. Hence, decreasing customer churn is the main business target of every online business. To manage customer churn, churning customers should be identified first. Then these customers must be convinced to stay.

To obtain the churn rate, associate the churning variable per case after profiling. And then calculate the percentage of churning customers per cluster.

Churn prevention demands the estimation of prospective customers on time and accurately. Therefore, the prediction is the prevalent method to decrease churn. Sales specialists do prediction by using limited variables and temporary rules and algorithms on the available data. Instead of humans, a machine learning model can be used that can sustainably educate itself and revise itself in the customer's business cycle direction.


How Does Prediction Works?

The common way of predicting future customer churn starts with your historical data analysis with B2Metric Data Ingestion tool. 

The data selection policy starts with describing variables that can affect customer churn. B2Metric Churn Prediction product predicts the reply for customers which already exist. It does that by setup an AutoML approach that ties up the predictors to replies. This model pertains to the administer learning situation. Predicting future customer churn is important because it helps your business' winnings a better realization of future expected income. Also, it makes easier for your business to increase in a better way and interpret long term hopes for income.

Besides, when your business can use churn prediction to flow potential churn for the percentage of your customer, it let able to you who does inference to keep them attention from stopping their connections with you.


Insurance Customer Churn Prediction

The Churn prediction model forecasting a customer's tendency to churn by using knowledge about the customer such as household and data like financial data, transactional data, and behavioral data.

The inputs for the Churn prediction model are customer demographic data, insurance diplomacies, prizes, terms, demands, grievances, and the sentiment total from past surveys.

The first step of data preparation for churn prediction is collecting all existing information about the customer. The data that is procured for predicting the churn is categorized in the following :

Demographic data, such as age, education, gender, revenue, employment condition, marital condition, homeownership condition, and retirement strategy.

Diplomacy-related data, such as insurance lines, number of diplomacies in the household, household time, prize, disposable revenue, and insured cars.

Claims, such as claim settlement duration, number of claims that are filed and denied.

Complaints, such as a number of open and closed complaints.

Survey sentiment data. Sentiment scores from past surveys are captured in the latest, and average note attitude score fields. The note attitude score is derived from customer negative feedback only. If the note attitude is zero, the customer is more satisfied while as the number increases, satisfaction level decreases.



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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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