Russian insurers that cooperate with the financial and credit sector find themselves in a difficult situation. On the one hand, against the backdrop of declining sales of voluntary corporate insurance, they benefit from increased sales of policies for registration of collateral. And it is justified by the growing demand for loans.
On the other hand, during a crisis, the number of unreliable clients increases, who, having acquired more than an acceptable level of debt, are trying to improve their financial situation by paying out false insurance events. As a result, collateral insurance for legal entities in 2015 alone significantly “gained” in terms of unprofitability. But insurers are not ready to refuse the flow of customers ready to buy a policy.
Insurance scoring: first steps in Russia
A way out of the situation was suggested by an intermediary - NBKI (credit history bureau). Its director Alexey Volkov told at the end of February how his organization can help the insurance market in the field of insuring the property of enterprises pledged as collateral.
According to the expert, a similar situation was observed in Europe 12 years ago. Then in developed Western countries (Russia followed suit in 2014), the legislator was forced to open the insurance company's access to credit histories for citizens and, more importantly, enterprises.
One of the largest analytical companies, FICO, has developed a specific product for large insurers - an insurance scoring model similar to credit scoring. What is this?
Scoring is a technology for determining the likelihood of a creditor (policyholder) defaulting. But unlike the classical method of assessing insurance risks, it takes into account behavioral factors - the level of responsibility of the subject and his willingness to answer for obligations under any circumstances.
In 2014, a law was passed in Russia that allows insurers to follow the same path,
- gain access to the history of loan servicing by enterprises;
- analyze the quality of behavior in relation to the contract with the lender;
- draw a conclusion about the good faith of the policyholder in order to offer him rates for collateral insurance based on the data received (or refuse to issue a policy).
Alexey Volkov said that in 2014 the need for such scoring technology in the Russian Federation was not as high as it is now. A noticeable reduction in the flow of policyholders (including corporate ones) has not yet been noticed; insurance companies covered their risks through streaming fees.
But today, when every contract needs to be checked, insurance scoring is again gaining relevance. And NBCH, with the assistance of FICO specialists, have already developed a model for its calculation. The principle of its operation is simple:
- Based on data on loans of a legal entity, the system determines the level of its loyalty;
- the result is obtained in points, the range is from 350 to 850;
- the lower the score, the higher the cost of insuring the collateral for the company and vice versa.
Who needs insurance scoring in a crisis?
This approach is beneficial for the insurer for two reasons, Volkov is sure. Firstly, the insurance company receives accurate data to calculate its risks. Secondly, it can cut off disloyal (unreliable) clients and reduce the likelihood of fraud on the part of the policyholder.
The latter innovation will also be interesting,” says the head of NBKI.
- The borrowing company (past or present) will be able to count on a discount on corporate property insurance, provided that past loans are properly repaid.
- The presence of a transparent methodology for assessing the prospects of the policyholder will simplify the process of approving the insurance budget.
The future of insurance scoring
Alexey Volkov is confident that it is for these reasons that already in 2017-2018, insurance scoring will be used at all levels of corporate insurance, not only for insuring collateral for a business loan. The expert also said that the effectiveness of scoring data has already been tested in 10 cities of the country, including Moscow.
As part of the pilot launch, the system assessed the long-term unprofitability of borrowers with a CASCO policy. The results showed that policyholders with scores below 625 are more unprofitable.
Let us remind you that our partners - the largest insurance companies on the market, which are on the lists of approved insurers for all banks - work with our clients on the terms of minimum tariffs in corporate insurance. The SA "GALAXY insurance" defends the honest relationship of the parties and the interests of the policyholder, and not the insurer.
In what types of insurance is scoring relevant?
So far, only credit history bureau scoring has received widespread use in Russia - in motor insurance. The identified dependencies make it possible to significantly clarify the forecast of unprofitability under a comprehensive insurance policy and even counteract attempts at property fraud. For example, the comprehensive insurance tariff depends on the age, gender, marital status of the car owner, the brand and region of operation of the car, as well as other parameters that insurers call the tariff factor. According to Vladimir Novikov, Deputy General Director, Risk Director - Head of the Actuarial Calculations Department of Sberbank Insurance, this is scoring. With the development of digital technologies and the accumulation of large volumes of data, it has become possible, in addition to the classic risk assessment factors, to use those that previously did not attract the attention of underwriters. The scoring technique is applicable not only to risk assessment: it works well in solving problems of marketing, sales, optimization of claims settlement, and combating fraud, believes Vladimir Novikov.
According to Head of the Marketing Research Department of IC "MAKS" Evgeniy Popkov, in the recent past, insurance scoring was a very limited toolkit. Thus, in most cases, employees of sales offices used insurance calculators for voluntary types, in which the control was triggered by certain triggers - “Underwriter approval required” or “Insurance check required.”
Alexander Morozov, Director of Statistics and Analytics, Smart Driving Laboratory, states that scoring is essentially a personal assessment of insurance risk. This estimate is more accurate compared to traditional models calculated based on averaged factors.
Alexey Danilov, CEO of Adaperio, gives the following example. Traditional assessment methods have always been based on the behavior of the average user - an abstract policyholder of a certain socio-demographic profile, but in fact the behavior of, for example, two 35-year-old men living in Moscow and using a BMW can be radically different. It is in this case that big data becomes useful, which will make it possible to more accurately determine the risks of the insurance company and, as a result, affect profit (loss) indicators.
How to learn to detect auto insurance fraud using machine learning methods? About this using the example of a scoring model with lift equal to 4. Ilya Lopatinsky, Director of the Retail Business Support Department at Ingosstrakh, will speak at Scoring Days 2018.
In world practice, scoring is used in all lines of business of insurance companies. In Russian practice, scoring is most common in such types as voluntary health insurance and car insurance, says General Director of BKI Equifax Oleg Lagutkin.“The most exotic type of application of scoring in our practice was assessing the propensity to fraud of insurance company employees who make decisions on the terms of concluding an insurance contract,” says Oleg Lagutkin. In his opinion, it is advisable to introduce scoring into processes such as antifraud, losses and sales.
Deputy Director of the Underwriting and Product Management Department of Soglasie Insurance Company Andrey Kovalev sees the potential for using scoring in all voluntary mass types of insurance (including auto insurance, voluntary health insurance, personal income insurance). The main area of use of scoring is risk assessment and anti-fraud, but it can also find application in the field of sales support.
Deputy General Director of VTB Insurance Evgeniy Nisselson believes that scoring is more appropriate to use in sales of retail products, such as auto insurance, property insurance, accident insurance, etc. It allows you to reduce the cost of risk assessment and significantly speed up the process. Scoring is applicable to standard products; to analyze specific risks it is necessary to use traditional methods.
Maria Barsova, Operations Director - Deputy General Director for Property Insurance of SAO ERGO, said that the company uses credit scoring in comprehensive insurance and individual insurance for individuals, mainly in underwriting and to determine tariffs.
Insurers are testing telematics
According to Dmitry Rykov, head of the underwriting department at auto insurance LLC Zetta Insurance, telematics-based policies have not yet received large-scale development, but the company continues to carefully test these products, monitor the market and is preparing to make an interesting offer. IN SC "Soglasie" also confirmed that the implementation of scoring on these telematics devices is under development and testing. IN "VTB Insurance" reported that the insurer does not use scoring based on telematics data on an industrial basis due to its limited presence in the auto insurance market. At the same time, the company tested telematics systems from different manufacturers and the results showed quite high efficiency. Maria Barsova, Operations Director - Deputy General Director for Property Types of Insurance of SAO ERGO, said that the company introduced scoring based on data from telematic devices and continues to do so, but it cannot be said that expectations were met 100%. The volumes are still small, and therefore it is too early to talk about the impact on unprofitability.
“Any data is useful for improving the assessment of personal insurance risk. Moreover, if they correlate well with this same risk and have no analogues. Data from telematics devices received directly from the car cannot be qualitatively replaced by other factors and correlates well with insurance risk, noted Alexander Morozov, Director of Statistics and Analytics, Smart Driving Laboratory. – Therefore, we can say for sure that telematics data is useful for scoring. The result of implementation depends on the specific model proposed by the insurance company, the composition, quality and cost of the data itself, so it would be incorrect to name any single assessment.”
What data should be used when constructing the scoring for comprehensive insurance? About this in the speech Frank Shikhaliev, head of the data analysis development department at Renaissance Insurance on April 19 at Scoring Days 2018.
Technologies: what insurers use
When asked whether the company uses its own developments or those of third-party suppliers, the company "Agreement" stated that they use both of these approaches. “Undoubtedly, with internal developments, higher business sustainability is ensured, but there are still areas where the company cannot carry out all developments on its own,” said Andrey Kovalev, Deputy Director of the Underwriting and Product Management Department of Soglasie Insurance Company. VTB Insurance Company uses ready-made solutions from suppliers, customized to the needs of the insurer. Company work "Sberbank insurance" within the framework of scoring, it can be divided into two parts. One part is analysis, which uses software and statistical packages that third-party contractors have developed for the company. The second part - the remaining 50% of success in using scoring data - is determined by the competence of employees, that is, it depends on the presence in the company of specialists who can work with big data.
Head of underwriting department in auto insurance of Zetta Insurance LLC Dmitry Rykov said that in addition to its own methods, the company uses tools provided by partners. One example is the Audatex service, which allows you to check the accident history of a car. Another example is the CBM for compulsory motor liability insurance, which also allows you to roughly assess the client’s insurance history.
Scoring insights from insurers and developers
The choice of car model really carries information about the client’s behavior on the road. For example, a client who has chosen a brand of vehicle that emphasizes the driving properties of cars consistently gets into accidents more often than a client who chooses a vehicle of the same class, power, size and cost, but from a manufacturer that emphasizes comfort or reliability, said Andrey Kovalev, Deputy Director of the Underwriting and Product Management Department of Soglasie Insurance Company.
Cases of scoring in car insurance from Ilya Lopatinsky from Ingosstrakh and Frank Shikhaliev from Renaissance Insurance - at the conference Scoring Days 2018.
According to Dmitry Rykov, head of the underwriting department at auto insurance LLC Zetta Insurance, there are many interesting dependencies: for example, the frequency of accidents for policyholders in different family statuses varies significantly. Thus, married drivers have the lowest frequency of insurance claims and receive a discount from the company. Another relationship that the company discovered directly in Moscow is the relationship between the probability of an insured event and the permanent registration address of the policyholder. The discount for a car owner living in an area with safer traffic can be 20% of the policy cost.
Vladimir Shikin, Deputy Director of Marketing at NBKI, reported that, as a rule, all patterns have a logical explanation, but it happens that they are discovered after the fact. For example, during testing, the company noticed that in a segment with low bank scoring values, there is a high probability of loss from theft. “We made the assumption that in this range there may be clients to whom, due to low responsibility, banks no longer give loans, and these people can solve their financial problems at the expense of insurance companies. That is, in essence, we have identified an indicator of potential fraud,” said Vladimir Shikin.
, Marketing Director of the National Bureau of Credit History (NBKI)Date of publication: 02/17/2016
Category: Secrets of the profession
When it comes to the concept of risk in relation to the financial sector, first of all, the retail lending segment comes to mind for many. And we are talking, accordingly, about credit risk. At the same time, in lending, risk has long been learned not only to assess, but also to manage it. Credit risk is calculated using predictive techniques for assessing the probability of a borrower's default in the future. Specially developed scoring models have been successfully coping with this for many years.
As for insurance, even for some players in this market it is still a surprise that it is possible to determine the risk of unprofitability of a policy by analogy with calculating credit risk. That is, using the same scoring. The loss ratio of the policy, that is, the ratio of payments for insurance events to the collected premium, is the target risk variable in insurance and, at first glance, has nothing to do with loan default. But in fact, both of these events have a common nature - lack of accuracy and neglect of their own obligations on the part of the subject.
With the introduction of amendments to the law “On Credit Histories” a year and a half ago, insurance companies now have the opportunity to obtain the credit histories of their clients. Since for insurers, as well as for lenders, credit histories are of the greatest interest precisely from the point of view of the possibility of assessing risk, the insurance industry and, accordingly, the NBKI (which stores the credit histories of 74 million Russians) faced the question of constructing a mathematical model, predicting unprofitability based on data from credit histories - insurance scoring.
This dependence has long been discovered and actively used by insurers in different countries. In Russia, such a correlation was known before, but could not be used in practice until 2014: the Law “On Credit Histories” did not allow credit history to be provided to non-creditors. Almost immediately after the amendments came into force, work began to formalize the mentioned dependence. The work was attended by NBKI experts, actuaries from major insurance companies and specialists from FICO, the author of the most popular and effective insurance scoring in the world.
By mid-2015, more than 5 million insurance policies had been processed and the match with the credit history database was about 80%. Insurance scoring, calculated on the basis of credit histories, as in retail lending, takes into account the quality of servicing of loan obligations, types of loans and history of use of borrowed funds. For ease of use, NBCH and FICO retained the scoring model scale - from 350 to 850 points. In this case, a low score means a high risk of unprofitability of the policy, and a high score - vice versa.
The results of testing the model on real auto insurance policies turned out to be comparable to credit scoring: CASCO, for which the model calculated a low scoring score (less than 625), turned out to be 20% unprofitable than policies with a high score (more than 725). This result was confirmed both for Moscow policies and for regional ones. Even more clear results were obtained when analyzing the loss rate from specific insurance events. For example, for damage from car theft, the loss rate of policies in low scoring ranges is 5 times higher than for the upper range. Obviously, this is due to the fact that the NBKI insurance scoring was able to identify unscrupulous citizens to whom banks had already stopped lending money due to their poor payment discipline and high debt load, and they went to insurance companies, hoping to solve the problem with the help of insurance payments and deception. your financial problems. In other words, NBKI insurance scoring has proven useful in preventing insurance fraud.
And finally, the success of the work done in auto insurance allows us to hope that similar technologies will be applicable in other types of insurance. According to NBKI and large insurance companies, the search and validation of dependencies between a person’s responsibility and his behavior for most insurance products is a matter of the near future.
Dedicated to the possibilities of scoring in the financial sector. Bankers and microfinance organizations shared successful cases, and IT companies and mobile operators talked about new opportunities. Unfortunately, there was not a single representative of the insurance industry among the speakers at the conference.
Is scoring, as an analysis tool, really not interesting to the insurance community? Quite the contrary. But if banks have long mastered this technology for analyzing the client base and widely use it when lending, then the insurance market is not yet so spoiled by this method of client selection. Nevertheless, to one degree or another, insurance companies still turn to this tool to create more adequate underwriting.
Five years ago, insurers did not use scoring tools at all. Three years ago they began timidly trying to use credit scoring in connection with the “engine”. Today, credit scoring can already serve as one of the key metrics in auto insurance underwriting and is gradually used in working with other types of insurance.
We, just like banks, want to know our clients in person. In order to correctly form a reserve and set a tariff, it is very important to understand what kind of person is in front of you, what can be expected from him, how unprofitable a particular client can be. Numerous studies conducted in the financial institutions market have already proven that if a person is undisciplined in one area of life, then he is likely to be undisciplined in other areas. Financial discipline, behavioral patterns and habits - this is what banks have been interested in for a long time, and now insurers should rightfully be interested as well.
There are a lot of data sources for collecting information: from credit bureaus to social networks, which can tell a lot about the client. The choice of these sources is determined by the specific needs of the company, budget and functionality of IT systems.
But the most important question is not what data to analyze (there are indeed many options and sources now), but how to do it. It is the correct interpretation of data, placement of emphasis and weights that allows you to build a working scoring system, which will not only help you understand the potential unprofitability of a particular client, but will also allow you to identify fraudsters who can lead the company to serious financial losses.
According to FICO and NBKI, which are actively conquering the niche of insurance scoring based on credit history data, clients with a comprehensive insurance policy with a low scoring score show a loss rate several tens of percent higher than those with a high scoring score. Having such data, by how much can the insurance company reduce the unprofitability of the portfolio? It is difficult to give a definite answer to this.
This indicator largely depends on the insurance segment and especially on how exactly to use the scoring result (refuse insurance altogether, offer an increasing coefficient, or something else). In marginal forms, it can reach several percent, and if a company’s portfolio amounts to billions of rubles, then the benefit can amount to several tens of millions.
The second difficulty is cost. Despite the fact that over the past few years the price of analysis of one client has decreased almost threefold (different data operators have different prices), scoring is still used mainly only in car insurance. Thanks to high margins, this is where the additional costs of analyzing the customer base are justified. For other types (property or accident insurance), scoring is still used more as part of experiments rather than for real savings.
The justification for scoring costs is also related to the volume of the analyzed portfolio. Our country has a fairly high level of debt among the population; the volume of loans continues to grow, even despite the fall in real incomes. At the same time, the penetration of insurance services is extremely low. Only this year we began to gradually increase the share of penetration in property insurance and life insurance. But this, of course, is not enough.
If the market manages to overcome at least one of these obstacles, then scoring in insurance will most likely cease to be almost a fantasy, becoming an effective stage of high-quality underwriting. After all, the potential of this tool is really very high.
- Services and products for credit institutions
Credit reports
Formation, processing and storage of credit histories
The organization that issued the loan is obliged, according to the law on credit histories, to provide to the accredited credit history bureau information about the borrower, as well as the amount of the loan he received, and the borrower’s consent to carry out such a procedure is not required. Thanks to this rule, the formation of credit histories is carried out in an extremely short time and allows you to create the most complete database, which stores information about all credits and loans received by the borrower. The lender provides information to BKI on the basis of an agreement for the provision of information services. Five working days are allotted for the transfer of data from the creditor to the bureau.
* Information can also be transferred to NBKI without expensive automation of your own processes, significant material costs and hiring special personnel. All you need to do is install the special “One-Click Transfer” application.Providing credit reports
On-line: Interactive interface (for credit institutions with a small volume of issued loans, the decision to issue which is not made immediately, considering 300-500 applications per month) - using this method, the operator registers in the system, fills out a form, sends a request, receives a loan on-line report in PDF format, studies the credit report and makes a decision on issuing or refusing a loan
On-line - B2B (for credit institutions with a large volume of issued loans, making a decision to issue a loan in a short time) - this method allows the bank to automatically request and receive credit reports in XML file format. By using it, you can integrate information from your credit report into the bank's automated decision-making process.
Batch request (for credit institutions that decide to issue loans within one or several days) - a batch request is generated by banks in the form of an XML file and sent to the bureau by e-mail. The response is generated by the bureau within 24 hours and sent to the bank in the form of an XML file containing all credit reports.
NBKI Online
The optimal solution for organizations starting lending activities or conducting moderate lending activity. The functionality of NBKI Online ensures full interaction with the Bureau, and does not require investment in the creation and support of hardware and IT software.
“NBKI Online” will allow you to receive data on borrowers from NBKI at the best price; transfer data to NBKI - data received from NBKI Online users is processed with the highest priority; keep records of data exchange with the bureau.
SCORING
Scoring Bureau
A risk measurement tool that evaluates a borrower's ability to meet his or her loan repayment obligations based on credit bureau records of the borrower's past behavior. The scoring model allows you to: predict non-compliance with the borrower’s payment obligations; rank borrowers according to their likelihood of falling into arrears.
The borrower's scoring value is calculated solely on the basis of the information contained in the credit history, which is converted into a scoring score ranging from 300 to 850, so that conscientious payers are assigned the highest score and dishonest payers the lowest. The scoring score is presented with the four reasons that had the greatest impact on its reduction.
Advanced scoring
Allows you to assess the default risk of borrowers without a credit history based on their socio-demographic data. When calculating the extended scoring score, characteristics such as age, marital status, place of residence, place of work, length of service, salary and other characteristics are taken into account.
Fraud score
A unique scoring model that allows you to assess the likelihood of an increased risk of issuing a loan based on the personal data and credit history of the potential borrower. The model was developed by the leader in predictive analytics, FICO®, based on processing millions of specific credit applications and credit histories. The model is characterized by high predictive accuracy, ease of integration into the lender’s existing borrower underwriting systems, and the ability to be managed on the lender’s side.
MONITORING OF THE BANK'S CLIENT BASE (ANALYTICAL REPORTS)
Analytical reports are generated based on information from NBKI databases and provide monitoring of the main parameters characterizing the state of the loan portfolio and the behavior of bank clients.
Analytical reports are provided monthly for a period determined by the lender. At the client's choice, the following reporting periods can be offered: quarterly, six months, yearly.
Reports are needed for: risk forecasting; determining the share of “risky borrowers”; assessing the loyalty and behavior of borrowers in other banks.
NBKI-AFS (COUNTERING UNCONSCIOUS BORROWERS)
The unique system for countering unscrupulous borrowers "NBKI-AFS" is the most modern, innovative and effective tool for protecting the lender from the actions of unscrupulous borrowers of various types.
The NBKI - AFS service was created with the direct participation of leading banks in retail lending and takes into account enormous practical knowledge about countering unscrupulous borrowers. Its integration into banking automated application processing systems is simplified as much as possible, which means that any bank can connect to the service quickly and at minimal cost. "NBKI - AFS" can be customized according to the specific requirements and characteristics of the user bank.
The service is “able” to carry out sequential and recursive comparison of attributes of loan applications and their analysis using “fraud” rules, identifying suspicious potential borrowers. The data undergoes a unique logical check using more than 160 rules, the effectiveness of which is confirmed by analysis carried out on the application databases of the largest retail banks. "NBKI - AFS" allows you to process more than 200 applications per second.
VERIFICATION OF CLIENT PASSPORT DATA
A service that provides real-time verification of the borrower’s passport.
Using the service, you determine the status of the document being checked, and also receive additional information available in the source databases.
ANALYTICS
The National Bureau of Credit History (NBKI) offers a wide range of risk analytics, allowing lenders to make informed strategic and tactical decisions:
Reports Benchmarking and Benchmarking of MFOs
They provide an objective picture of the lender’s position relative to the depersonalized pool of lenders in the competitive group. The group is formed by the customer of the Report independently, but requires mandatory approval from NBKI. The number of creditors in the group is from 3 to 5. Lenders must be comparable in business size.
The report presents 230 indicators: portfolio size; portfolio quality; quality of incoming population, approval levels; overdue: recovery from overdue and quality of return (collection); portrait of the borrower by type and size of loans, age of borrowers, regions of Russia, FICO scoring ranges (buckets), etc.
The data is presented in general for loans to individuals and broken down by credit products that have the largest share in the structure of retail lending in Russia.
Each parameter is presented in dynamics over the last year.
Benchmarking.Collection report
“Benchmarking.Collection” reports provide an objective picture of the effectiveness of collection procedures based on a comparative analysis with a reference group and allow you to find segments that require adjustments to the work of the relevant divisions of the creditor/collector. Groups of banks for comparison may be different for each loan product; The list of banks being compared is determined by agreement with the NBKI and must meet the requirements for their homogeneity in terms of market volumes and product niches. Thus, their trade secrets and business ethics rules are not violated. The number of compared banks should not be less than 3 and more than 5.
The report consists of two files with corresponding sections:
1. “Benchmarking Early Collection” file
2. “Benchmarking Late Collection” file
The data is presented broken down by credit products that have the largest share in the structure of retail lending in Russia. Each parameter is presented in the context of Issue amounts, Issue regions, FICO2 AM ranges, Overdue periods in days (0 – 4th buckets).
National Credit Bulletin
The National Credit Bulletin is Russia's only quarterly review of retail lending, providing a detailed picture of trends and risks in the sector. The review presents the dynamics of lending indicators in the country as a whole and in the largest regions of the Russian Federation, by type of loan.
Provided in paper and electronic form (MS Excel).
Analysis of the debt burden of Russian borrowers
This review uses two types of indicators characterizing the debt burden of the population: the ratio of the debt balance to the borrower's annual cash income and the ratio.
The review is compiled twice a year. You can subscribe to a one-time study or 2 reports per year.
PARTNERSHIP AGREEMENT. PROVIDING CREDIT HISTORIES TO ENTITIES
Selling credit histories to Bank clients is a modern service that ensures an increase in commission income.
Most Russians would like to receive their credit history at a bank branch. Indeed, today about 90% of credit histories from the NBKI database are sold in bank branches and through the remote banking system of NBKI partner banks.
As part of the partnership agreement, the Bank can provide services for providing a credit report from the NBKI database, as well as several additional services: access to the database of collateral movable property, a report from the Central Credit Inspectorate, etc.
The National Bureau of Credit History (NBKI) appreciates the work of its partners in this type of agreement and provides all possible support for the development of this business.
CHECKING THE CAR ON A UNIFIED DATABASE OF PLEDGED CARS AND OTHER MOVABLE PROPERTY
The sources for the formation of the “Base of pledged cars and other movable property” of the NBKI are banks and other creditors that cooperate with the NBKI and transmit information about the vehicles they have as collateral.
Composition of the notification in the “Database of pledged cars and other movable property” of the NBKI.