B2Metric brings you the latest in artificial intelligence and credit underwriting. 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.
What Does Underwriting Mean ?
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.
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 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 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
- 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.
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.
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.
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.
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.
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.