In every business, from the small corner store to the large manufacturer, there are common challenges with insurance, claims, and risk in general. Buildings can be damaged by fire, someone could slip and fall, vehicle accidents often occur, or losses can occur as a result of defective products. Risk management steps in here. Our business ventures encounter many risks that can affect their survival and growth. As a result, it is important to understand the basic principles of risk management and how it can be used to help mitigate the effects of risks on business entities.

Risk analysis and risk management is a process that allows individual risk events and overall risk to be understood and managed proactively, optimizing success by minimizing threats and maximizing opportunities and outcomes.

Risk management encompasses the identification, analysis, and response to risk factors that form part of the life of a business. Effective risk management means attempting to control, as much as possible, future outcomes by acting proactively rather than reactively. Therefore, effective risk management offers the potential to reduce both the possibility of a risk occurring and its potential impact.

The extent of risk can be expressed as follows:

Risk = Probability x Severity

Probability is the likelihood of an event occurring, and severity is the extent and cost of the resulting loss. In broad terms, risks may be broken down into two categories:

Pure Risk - Risks where the possible outcomes are either a loss or no loss. It includes things like fire loss, a building being burglarized, having an employee involved in a motor vehicle accident, etc. Speculative Risk - Risks where the possible outcomes are either a loss, profit, or status quo. It includes things like stock market investments and business decisions such as new product lines, new locations, etc.

Importance of Risk Management

1- The first impression: Home page

For a business, assessment, and management of risks is the best way to prepare for eventualities that may come in the way of progress and growth. When a business evaluates its plan for handling potential threats and then develops structures to address them, it improves its odds of becoming a successful entity. In addition, progressive risk management ensures the risks of a high priority are dealt with as aggressively as possible. Moreover, the management will have the necessary information that they can use to make informed decisions and ensure that the business remains profitable.

Risk identification mainly involves brainstorming. A business gathers its employees together so that they can review all the various sources of risk. The next step is to arrange all the identified risks in order of priority. Because it is not possible to mitigate all existing risks, prioritization ensures that those risks that can affect business significantly are dealt with more urgently.

In many cases, problem resolution involves identifying the problem and then finding an appropriate solution. However, prior to figuring out how best to handle risks, a business should locate the cause of the risks by asking the question, “What caused such a risk and how could it influence the business?”

Once a business entity is set on assessing likely remedies to mitigate identified risks and prevent their recurrence, it needs to ask the following questions: What measures can be taken to prevent the identified risk from recurring? In addition, what is the best thing to do if it does recur?

Here, the ideas that were found to be useful in mitigating risks are developed into a number of tasks and then into contingency plans that can be deployed in the future. If risks occur, the plans can be put into action.

Risk Management For Insurance Industry

One of the insurance company’s core competencies is providing customer service to those who need to submit a claim. If customers consistently have poor customer service experiences, they’re likely to share their stories on social media, tarnish the company’s reputation, and the company will fall behind the competition. According to a recent study, the National Association of Insurance Commissioners (NAIC), core risks in the insurance business include “underwriting, credit, market, operational, liquidity risks, etc.” Given this wide variety of concerns, there is a tremendous opportunity for risk management in insurance companies to make a positive impact.

To return to the customer service example above, let’s look at how enterprise risk management could help. Risk management involves identifying, assessing, and mitigating risk. The beauty of a well-implemented risk management program is it’s built on a foundation of standardized risk assessments to help companies prioritize their risk based on its potential impact. Naturally, this process will surface risks that will impact the business’s core competencies. For an insurance company, customer service would inevitably come to the forefront of a risk assessment. To address this risk, the insurance company could take steps to integrate incident management and risk management. Most companies have a way to track incidents like customer complaints, but many do not have a way of categorizing, prioritizing, and escalating incidents across teams. Risk management in the insurance business helps centralize and identify trends in customer feedback. From there, insurance companies can implement controls to address those trends, such as hiring more customer service reps to resolve long wait times or implementing call-screenings to identify less-than-helpful interactions. Improving customer service is only one example of how insurance companies can leverage risk management. A fully integrated enterprise risk management program can help insurance companies develop proactive mitigation activities to protect the core of their business.

Thanks to the AutoML technology offered by B2Metric, you can do all these steps in the easiest and fastest way, with accuracy. B2Metric automates the data preparation, feature engineering, and model selection process for you with supervised, unsupervised, semi-supervised algorithms.

For details see the B2Metric Hunter Insurance Risk Assessment Engine

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Did you know that 49% of consumers said they have purchased a product that they did not initially intend to buy after receiving a personalized recommendation? Actually, without going further with the research you can just look at your own experience. Nobody clicks a website and leaves it with just one shirt purchased anymore. And customers actively want that experience. 52% of consumers say they would share personal data in exchange for product recommendations.

Dynamic pricing is adjusting products’ prices throughout the day. In a way, it’s making sure to have optimum prices while variables are changing. The main reason to use this method is increasing product profit and chances of sale at the same time. Dynamic pricing is the fast alternative for fixed prices. COVID-19 showed us the world can change so much in a little time. So instead of having set prices for a period of time, companies can adjust the ever-changing market with updating their prices multiple times per day.

Sometimes dynamic pricing gets confused with personalized pricing. The difference is that dynamic pricing looks at the bigger picture while personalized pricing focuses on an individual. So dynamic pricing analyzes your products and their relative value in relation to the rest of the market. Personalized pricing, on the other hand, looks at individual consumer behaviors and changes the product’s value based on previous shopping experience of that particular consumer.

How to use product recommendation in E-Commerce

1- The first impression: Home page

It’s hard to make recommendations for new customers so your home page should be more generic and leading. Featuring best-selling items makes customers feel more secure. It shows them there are people already trusting you. Recommending best-selling products on the homepage has shown to be a highly-effective tactic for hooking your users’ attention as soon as they reach your site. Also, best-selling section should always be updated. Remember, %20 percent of your items will provide %80 of your sales. Social proof is a big element in online shopping. People look at reviews and read comments to make sure they get what they actually want. Showing the highest-rated items will guarantee the quality of your products. 37% of shoppers that clicked a recommendation during their first visit returned, compared to just 19% of shoppers that didn’t click a recommendation during their first visit. Holidays are not magical times only for the children, but also for e-commerce. Use product recommendations to remind customers of the coming holiday season. It may lead them to quickly get holiday shopping out of the way.

Product pages are the best places for upselling. Use product recommendations for moving the buyer up to a more fully-featured version of the one currently being browsed. If there isn’t a better version of the same thing you can always try something slightly different but more expensive. Sometimes the search for a new book can lead us to buy a set of 12 books. Or a t-shirt may become a t-shirt skirt combine. To make those happen you can use “Frequently bought together” recommendations. It differs from the shopping cart recommendations by the price. At the shopping cart, you offer side pieces like socks but here you can offer more expensive jeans for a sweater. “Customers who bought [this item] also bought [that item]” recommendations provide social proof and peer-generated recommendations of relevant products the user may be interested in. For people, it’s like seeing their best friend wearing a new pair of sunglasses.

It’s your last chance to persuade customers to buy items. Luckily, shopping cart page recommendations can be very helpful. Because customers that clicked on recommendations are 4.5x more likely to add items to cart and complete their purchase. Displaying a list of suggested products based on the customer’s browsing history (“Recommended for you”) is an often-used and effective type of product recommendation. If you want to give a more personalized experience you can add the customer’s name to the title of the section. Since 75% of customers are more likely to buy based on personalized recommendations it would be a nice gesture. You can add related items under the main product. Provide product recommendations when items added to the cart require accessories (fishing reels need fishing line, flashlights need batteries, shoes often require socks). In addition to that, you can create groups of related items. Generating product groups (items frequently purchased together) and giving a discounted price for them could be another way of accessorizing.

No one wants to see an apology when they can’t get their dog’s favorite food. But it doesn’t have to be the end of the road. You can turn it into an opportunity. Instead of just saying sorry you can add a section of similar products, related products, and even the old items they added their cart and didn’t buy. One of them will catch the customer’s attention for sure.

The Core of Product Recommendation: Cross-Selling

Cross-selling is a marketing strategy that persuades prospective customers to purchase add-on products. Add-on products are very popular with health care and insurance providers, but they have been largely adopted by online retailers as well. Even if you don’t know what is cross-selling you have definitely seen it at least once. For example, if you try to buy a phone from an online shop, the website will try to cross-sell you phone cases, wireless headphones, etc. It works even better for fashion retailers. They cross-sell belts, hats, accessories, even t-shirts with just a pair of jeans.

The difference between cross-selling and upselling

Upselling is a marketing strategy that persuades prospective customers to purchase higher value products or upgrade a product or a service. If we go with jeans example, upselling means offering better quality jeans. Or instead of phone cases, it means selling the next model of the same phone. Cross-selling means fries with your burger, upselling means getting a bigger burger.

Benefits of Product Recommendation

One of the best things about shops is there are people to help you. Even without knowing what you need, you can just tell your problem and they will recommend products for you. It’s the whole point of product recommendation in e-commerce. You need happy and satisfied customers. Seeing belt options while looking for jeans is the experience customers are looking for. No one wants to wander around the shop to find items, they want them gathered together for them. Product recommendations improve not only sales but also customer experience. Happy customers mean more customers and increased sales. Recommendations help customers realize what they may need. A customer may forget batteries while purchasing a flashlight but if you offer them batteries probably they will buy them. And if you offer them a better flashlight then they are looking for, they may realize that’s the one they actually need. According to research, personalized product recommendations are estimated to account for more than 35% of purchases on Amazon. Upselling and cross-selling are a huge part of revenue anymore. Let’s say a customer purchased everything they needed and liked recommendations. What happens next? Happy customers knowing that they are important to you are much more likely to stay loyal to your service. This applies both to your already existing customers and newcomers. And happy customers can help you to increase customer retention. If they like your services and products, they can recommend it to their friends and family. In this way, you will get more users who will make more purchases. Obviously, this leads to higher profits.

How Do Ecommerce Product Recommendation Engines Work?

• Data collecting from various sources of product & historic price
• Defining goals
• Train the model & automated model selection
• Automated feature engineering to find best features
• Getting the optimized price for eacy products

Thanks to the AutoML technology offered by B2Metric, you can do all these steps in the easiest and fastest way, with accuracy. B2Metric automates the data preparation, feature engineering, and model selection process for you with supervised, unsupervised, semi-supervised algorithms.

Data Gathering & Cleaning with B2Metric

B2Metric automatically brings the data preparation feature. With this technology, it has improved the process of data collection & cleaning to a higher level and offered a new perspective and high-level experience to the users

Find the Best fit for each your customer with B2Metric Product Recommender

While creating a model with the B2Metric Machine Learning Studio product, you can easily select the target value and input from your ready data and easily run the model with these parameters.

Automated Model Selection

B2Metric works with supervised, unsupervised, semi-supervised algorithms, and automates the processes of champion model selection for data teams with AutoML.

Modeling & Deployment

All models that run with B2Metric ML Studio are prepared and interpreted for you. B2Metric interpretable ML models explain to you all causes and consequences, feature relationships, micro-segments, and outputs. Thus, easy and understandable models are created for relevant non-technical teams.

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Price optimization is the process of capturing the right pricing point. In other words, it is to maximize profit according to the purchase and payment demands of customers and competitors. It is of great importance for companies in all segments to increase their income by price optimization at every stage of their activities. Pricing issue is mainly a management decision. So, the question here is how is the price determined to gain maximum profit? The determination of the price seems pretty simple in theory. Income and expenses are examined depending on the demand curve and cost functions. Points, where revenues are equal to or greater than expenses, can be determined as prices. Many small companies still set their prices manually in this way. Even if this approach is economically meaningful, it is only this in theory. Many factors affecting the price, such as long-term customer behavior, anomalies, and the influence of competitors, are overlooked.

Dynamic pricing is adjusting products’ prices throughout the day. In a way, it’s making sure to have optimum prices while variables are changing. The main reason to use this method is increasing product profit and chances of sale at the same time. Dynamic pricing is the fast alternative for fixed prices. COVID-19 showed us the world can change so much in a little time. So instead of having set prices for a period of time, companies can adjust the ever-changing market with updating their prices multiple times per day.

Sometimes dynamic pricing gets confused with personalized pricing. The difference is that dynamic pricing looks at the bigger picture while personalized pricing focuses on an individual. So dynamic pricing analyzes your products and their relative value in relation to the rest of the market. Personalized pricing, on the other hand, looks at individual consumer behaviors and changes the product’s value based on previous shopping experience of that particular consumer.

Actual Price Optimization

Price optimization studies are carried out in many sectors such as retail, insurance, airlines, telecom, automotive spare parts by OEMs, and also finance. The factors that affect the price in real-world conditions and to consider in the price optimization process are:

• Retail or wholesale
• Customer consumption behavior in the long run
• Historical unit of sales prices of each of the product
• Promotion and campaign strategies
• Production, procurement, service, etc. costs
• Prices and campaigns of competitors
• Customer segments
• Economic variables
• Stock status
• Special days & events and season

In addition to all these conditions, there are also variations of the optimized price such as;

• Starting price
• Optimal (the best) price
• Discounted price
• Promotional price
• Campaigns

Price optimization allows companies to determine the above-mentioned prices. But these variations and the variability of factors affecting the price make it difficult to manually optimize prices. Thus, various price optimization tools and artificial intelligence & machine learning based price optimization studies have emerged.

How to Implement Dynamic Pricing Strategy

Implementing dynamic pricing can be very profitable when it’s done right. That’s why you should consider implementing dynamic pricing as an opportunity to improve your price optimization strategy and your overall margin. Implementing dynamic pricing can be tricky to begin with. So here is a five step process to help you through this journey:

1) Decide on your commercial objective:

Commercial objective is what keeps your company in the right way. It’ll help you handle any institutional changes. The commercial objective concerns more than just pricing and marketing, but it’s the main element of having a successful dynamic pricing strategy.

2) Have a pricing strategy:

Your pricing strategy uses your commercial objective to create a strategy that your team can use to sell products. For example, let’s say your commercial objective is to be known as the cheapest one on the market. Your pricing strategy would then be to make sure your products are always cheaper than the competition’s alternative.

3) Choose your pricing method(s):

Your pricing strategy shows you your future pricing goals. Your methods are how you'll achieve those goals. Your pricing methods should be more sharp and specific than your pricing strategy.

4) Set down pricing rules:

Pricing rules tell your dynamic pricing software what to do. You should set a rule for every product that the software needs to track and change.

5) Test and monitor:

Once everything’s all set up it doesn’t mean your job is done. You should keep track of the product sales, technical errors and effectiveness of the software you use. If it’s necessary you should make changes about it.

Importance of Price Optimization

Firstly, the number of decisions affecting pricing is increasing day by day in all industries. For this reason, more dynamic decisions are needed. For example; hundreds of new e-commerce sites are being established every day. The increase in competitors, consumer consumption behavior changes. Expenses will increase with the growth of the market. These are all sudden changes that affect pricing. With machine learning-based price optimization, you can be affected by these changes in the least possible way and protect your profitability.

Additionally, the best price may differ from customer to customer. This requires customization of each offer and price. For example, in holiday reservations, every service included in the package will change the price. For this reason, each customer should be given a different price according to his demand. Price optimization allows you to manage customized prices most profitably. Finally, it is important to make quick decisions, as the pricing process will be affected by more than one sector and will affect many industries. You can make the most effective decisions as soon as possible with artificial intelligence-supported price optimization tools.

Price Optimization with Automated Machine Learning (AutoML)

The price optimization process can be difficult, but it is not at all difficult to create a strong pricing strategy with the help of machine learning models. Machine learning-based pricing processes are of great importance nowadays, where customers can easily compare many prices offers with special search tools. AutoML, which has a great impact on KPIs, provides a fast and effective solution by learning the patterns given. Machine learning enables related units such as sales and marketing to develop complex strategies and optimize prices. A common example of ML price optimization is dynamic pricing. However, changes made with dynamic pricing may pose some problems. For this reason, dynamic pricing should be used together with price optimization techniques. As we mentioned before, considering the diversity of parameters that affect pricing, decision making features by reviewing many factors provided by ML is a big factor to use ML in price optimization. Machine learning algorithms allow you to reach the best decision by examining many parameters and situations. It is almost impossible to manually examine such a large number of parameters individually and accurately. Besides, with machine learning, you can analyze customer behavior in current prices, as well as predict customer buying behavior at possible prices.

The main steps of price optimization with ML are as follows:

• Data collecting from various sources of product & historic price
• Defining goals
• Train the model & automated model selection
• Automated feature engineering to find best features
• Getting the optimized price for eacy products

Thanks to the AutoML technology offered by B2Metric, you can do all these steps in the easiest and fastest way, with accuracy. B2Metric automates the data preparation, feature engineering, and model selection process for you with supervised, unsupervised, semi-supervised algorithms.

Data Gathering & Cleaning with B2Metric

B2Metric automatically brings the data preparation feature. With this technology, it has improved the process of data collection & cleaning to a higher level and offered a new perspective and high-level experience to the users

Defining Pricing Target with B2Metric Price Optimizer

While creating a model with the B2Metric Machine Learning Studio product, you can easily select the target value and input from your ready data and easily run the model with these parameters.

Automated Model Selection

B2Metric works with supervised, unsupervised, semi-supervised algorithms, and automates the processes of champion model selection for data teams with AutoML.

Modeling & Deployment

All models that run with B2Metric ML Studio are prepared and interpreted for you. B2Metric interpretable ML models explain to you all causes and consequences, feature relationships, micro-segments, and outputs. Thus, easy and understandable models are created for relevant non-technical teams.

Automative Spare Parts Wholesale Price Optimization

Research indicates that an average industrial or automotive company generates 10% of its revenues from spare parts sales and more than 40% of their profit. Given the profitability of the spare parts market, manufacturers have realized that this element is critical for company operations. Pricing is the key for harvesting the untapped potential of the spare parts market and is the best lever for improving profitability. Industry estimates show that a 1% increase in price can lead to an 11% increase in operating profit. By using the right pricing tactics for spare parts, manufacturers can realize significant increases in sales volumes, operating profit and customer satisfaction. Pricing of spare parts is challenging since each spare part has different competitors, consumption behavior and market potential. The most common pitfall in pricing is applying standard markup (cost-plus) pricing or competition-based pricing, both of which are attempts to elevate earnings without understanding the implications (i.e., failing to tailor the service delivery model due to lack of authority and resources to spare part managers). Instead, using a dynamic pricing software can maximize the profit.

A dynamic pricing software can identify the critical price points, analyze competitor’s pricing strategy and find out what customers would really pay for the piece. Then it can combine these with much more data and give you the optimum price. Also, it may seem like a long, big process but actually it can give you new numbers a few times a day -which is the optimum for changing prices. Everything that’s written above (and more) can be achieved with B2Metrics. To get more info you can visit products or simply contact us.

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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.