Whitepaper- Driving financial services as a telecom operator

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1 Whitepaper- Driving financial services as a telecom operator Marius Juvet, PrecisePrediction Nov, 2012 Background I would like to start this paper with the conclusion of the survey and results done by the Banana Skin report of The Banana Skin report is a yearly report published from The Centre for the Study of Financial Innovation and is a non-profit think-tank, established in 1993, led by Andrew Hilton. This year, our survey has identified another worrying trend a widespread perception that the industry could well find itself facing the kind of bad debt problem that many conventional financial institutions have had to cope with in the last few years. The reason is simple: too many clients of too many MFIs have taken on too much debt. Hard figures are difficult to come by and some observers of the industry believe that the worst of the problem is actually behind us. But the most striking result of this year s survey is clearly the very high risk ranking attached to over-indebtedness among MFI clients. Still, forewarned is forearmed and, whatever progress has been made to date, the industry (and the donor institutions that support it) now has no excuse not to tackle the problem. It is also worth making the point that this problem is one of success not of failure. It reflects the ubiquity of the microfinance model, and the way it has POVERTY IS THE DEPRIVATION OF FOOD, SHELTER, MONEY AND CLOTHING THAT OCCURS WHEN PEOPLE CANNOT SATISFY THEIR BASIC NEEDS. POVERTY CAN BE UNDERSTOOD SIMPLY AS A LACK OF MONEY, OR MORE BROADLY IN TERMS OF BARRIERS TO EVERYDAY LIFE. penetrated into those parts of the global credit market that others cannot reach. As the industry strives to retain its relevance in the face of big changes, this is one of its undoubted strengths. This point s out the most eminent challenge in handling lending in emerging markets, namely creditrisk. This is a topic, which banks and micro financing companies recognize as a complicated area. The problems lies in ID verification and in predicting customer payment behavior. The area of financial risk assessments have historically been handled by these banks, credit institutions, creditburaus and for emerging markets by micro financing companies, but that is now all about to change. Globalization is changing our world faster, and a lot of the global growth for financial services and telecom will come from emerging markets the next decade.

2 Using calling patterns to predict credit risk PrecisePrediction has a multidisciplinary background as both data mining consultants in a number of projects in the telecom area, while at the same time the company has more than 10 years experience in modern credit risk management for some of the world's most innovative credit card and unsecured loan companies. Some of these projects led us to part s of the world where people rather than underbanked, were unbanked. These areas were often on the brink to Poverty. Ever since, we here at PrecisePrediction have been working with a framework for handling lending and calculating creditrisk under these circumstances. After several years of analyzing and working, we concluded that the best approach was to utilize the infrastructure of the telecom operators. DATA MINING IS A FIELD AT THE INTERSECTION OF COMPUTER SCIENCE AND STATISTICS,AND IS THE PROCESS THAT ATTEMPTS TO DISCOVER PATTERNS IN LARGE DATA SETS. IT UTILIZES METHODS AT THE INTERSECTION OF ARTIFICIAL INTELLIGENCE, MACHINE LEARNING, STATISTICS, AND DATABASE SYSTEMS. The telecommunications industry was one of the first to adopt data mining technology. This is most likely because telecommunication companies routinely generate and store enormous amounts of highquality data, have a very large customer base, and operate in a rapidly changing and highly competitive environment. Telecommunication companies utilize data mining to improve their marketing efforts, identify fraud, and better manage their telecommunication networks. However, these companies also face a number of data mining challenges due to the enormous size of their data sets, the sequential and temporal aspects of their data, and the need to predict events such as customer risk assessments and fraud in real-time. The popularity of data mining in the telecommunications industry can be viewed as an extension of the use of expert systems in the telecommunications industry (Liebowitz, 1988). According to earlier studies, the three largest databases in the world did all belong to telecommunication companies moving up to Petabytes (PB). Thus, the scalability of data mining methods is a key concern. A second issue is that telecommunication data is often in the form of transactions/events and is not at the proper semantic level for data mining. For example, one typically wants to mine call detail data at the customer level but the raw data represents individual phone calls. Thus it is often necessary to aggregate data to the appropriate semantic level (Sasisekharan, Seshadri & Weiss, 1996) before mining the data.

3 Postpaid as credit risk exposure Credit scores are starting to being used by telecom service providers for granting postpaid connections - Your credit score and credit information are your reputational collateral, which reflects your credit behavior and provides an indication of your debt management capacity. Postpaid telephone connection is an alternative form of credit. Which means, through a postpaid connection, the telecom service provider gives customers an advance credit facility and trusts them to make the bill payment of their usage, by the due date. In some countries, telecom service providers are using credit scores and credit information for making instant decisions on postpaid telephone service applications to grant on spot telephone connections A CREDIT SCORE IS A NUMERICAL EXPRESSION BASED ON A STATISTICAL ANALYSIS OF A PERSON'S CREDIT FILES, TO REPRESENT THE CREDITWORTHINESS OF THAT PERSON to consumers. When you apply for a postpaid telephone facility, the service provider will pull your credit score and credit history, which gives the information provided by you in the application. Your score will enable the service provider to assess your financial standing and project your likelihood of paying the postpaid telephone bills regularly. Once the telecom service provider runs your customer (KYC) check on your documents and is satisfied on your payment capacity, they will be able to grant you the postpaid telephone service instantly. Credit score is also being used by telephone service companies to evaluate the risk appetite of the consumer and assign credit limit as per the credit behavior of the consumer. Value added services may also be offered to consumers based on their credit score. These are usually the financing of handset s and post payment of content like music and films. In developed economies, an individual s credit information report and credit score are very critical reputational collateral and is being used for multiple purposes by various institutions. Employers review it before recruiting a new employee; landlords require it before renting out an accommodation and of course telecom service providers check an applicant s credit history before providing a postpaid phone connection. In the future, a person s credit information report and credit score will be imperative for a lot many things in addition to availing institutionalised credit facilities. Therefore, it is important to maintain financial discipline and prudently manage all your financial obligations in order to build and maintain this vital reputational collateral that will become critical for a lot of transactions in the future.

4 Risk assements without credit history The challenges in emerging markets are that KYC and credit history do not exist. In many parts of the world neither good procedures for identifying people, nor the ability to track and merge the information from criminal records to employment and income exist. In these circumstances, lenders need other type of information to INSTEAD OF HISTORICAL REPUTATIONAL RISK, WE LEAN TOWARD THE EVENT TRIGGERED TRANSACTIONAL BEHAVIOR. evaluate the financial discipline of its customers. PrecisePrediction developed in the period specific lending strategies based on behavioral reputational models to overcome the lack of KYC and credit history. Today over 90% of the world banks uses old mathematical methods to handle their credit risk, and their transactional environment will not usually be able to support and utilize the lightweight KYC that our methods are based on. Instead of historical reputational risk, we lean toward the event triggered transactional behavior. PrecisePrediction emerging lending strategies are based on DOE (James Lind), and our behavior models are based on machine learning algorithm specially designed for regression problems. The mathematical penciling does not exist, so the ability to adapt from a broad range of techniques and mix them via an self-learning optimizer is the key values in being able to conduct viable lending in these areas. The future - Retail banking as a Telecom company PrecisePrediction gives Telecom companies a revolutionizing way of offering financial service to meet the needs of an estimated 2.7 billion people worldwide with a mobile phone but no access to formal financial services. There is a vast market of consumers in countries like Brazil, China, India, Russia and the Philippines who want access to financial services like credit cards, loans, or insurance. While they may have jobs, and some have bank accounts, there really have no credit history. The overall idea is that the way you use your phone is a proxy for your lifestyle, and thus your general behavior. With the holistic approach of PrecisePrediction we give Telecom companies 10 years of modern credit risk management for unsecured lending, and the ability to utilize their own large customer databases to be exposed to a full fletch retail banking services.

5 Solution highlights Through the PrecisePrediction solution we give Telecom companies the ability to - Move clients to postpaid, which increases loyalty - Financing of handset s - Money transfer with extremely low transaction cost - Unsecured loans from 5$ and upwards. - Asset financing ( animals, machinery) - Long term unsecured lending. - Social lending - Crowd financing - Access to international bank infrastructure ( IBAN/ SWIFT) Functionality To support this we use our fully automated lending platform that supports: - KYC (fully adaptable to support local differences) - Policyrules - Credit decision (different algorithms, but mostly based on Machine learning) - Both Application models and behavioral models - Credit matrixes - Uplift models - Decrease strategies - Overrides - Strategy testing segments - Fraud scoring - Collection scoring - Collection and paying behavior varies from country to country Capital allocation - Up to Basel III if needed. PrecisePrediction also assists in arranging local licensing, and handling compliance with laws and regulations for the telecom operators to act as financial lenders. Future trends Unsecured lending should play an important and increasing role in the telecommunications industry due to the large amounts of high quality data available, the competitive nature of the industry and the advances being made in credit risk. In particular, advances in mining data streams, mining sequential and temporal data, and predicting/classifying risk based on events should benefit the telecommunications industry. As these and other advances are made, more reliance will be placed on the knowledge acquired through data mining and less

6 on the knowledge acquired through the time-intensive process of eliciting domain knowledge from experts although we expect human experts will continue to play an important role for some time to come. Changes in the nature of the telecommunications industry will also lead to the development of new applications and the demise of some current applications. Contact us at: Preciseprediction AS Kjorbokollen Sandvika, NORWAY Tel: Web: References Aggarwal, C. (Ed.). (2007). Data Streams: Models and Algorithms. New York: Springer. Alves, R., Ferreira, P., Belo, O., Lopes, J., Ribeiro, J., Cortesao, L., & Martins, F. (2006). Discovering telecom fraud situations through mining anomalous behavior patterns. Proceedings of the ACM SIGKDD Workshop on Data Mining for Business Applications (pp. 1-7). New York: ACM Press. 490 Data Mining in the Telecommunications Industry Baritchi, A., Cook, D., & Holder, L. (2000). Discovering structural patterns in telecommunications data. Proceedings of the Thirteenth Annual Florida AI Research Symposium (pp ). Cortes, C., & Pregibon, D (1998). Giga-mining. Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (pp ). New York, NY: AAAI Press. Cortes, C., & Pregibon, D. (2001). Signature-based methods for data streams. Data Mining and Knowledge Discovery, 5(3), Cox, K., Eick, S., & Wills, G. (1997). Devitt, A., Duffin, J., & Moloney, R. (2005). Topographical proximity for mining network alarm data. Proceedings of the 2005 ACM SIGCOMM Workshop on Mining Network Data (pp ). New York: ACM Press. Fawcett, T., & Provost, F. (2002). Fraud Detection. In W. Klosgen & J. Zytkow (Eds.), Handbook of Data Mining and Knowledge Discovery (pp ). New York: Oxford University Press. Freeman, E., & Melli, G. (2006). Championing of an LTV model at LTC. SIGKDD Explorations, 8(1), Getoor, L., & Diehl, C.P. (2005). Link mining: A survey. SIGKDD Explorations, 7(2), Hill, S., Provost, F., & Volinsky, C. (2006). Networkbased marketing: Identifying likely adopters via consumer networks. Statistical Science, 21(2), Kaplan, H., Strauss, M., & Szegedy, M. (1999). Philadelphia, PA: Society for Industrial and Applied Mathematics. Klemettinen, M., Mannila, H., & Toivonen, H. (1999). Rule discovery in telecommunication alarm data. Journal of Network and Systems Management, 7(4), Krikke, J. (2006). Intelligent surveillance empowers security analysts. IEEE Intelligent Systems, 21(3), Liebowitz, J. (1988). Expert System Applications to Telecommunications. New York, NY: John Wiley & Sons. Mani, D., Drew, J., Betz, A., & Datta, P (1999). Statistics and data mining techniques for lifetime value modeling. Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp ). New York, NY: ACM Press. Masand, B., Datta, P., Mani, D., & Li, B. (1999). CHAMP: A prototype for automated cellular churn prediction. Data Mining and Knowledge Discovery, 3(2), Mozer, M., Wolniewicz, R., Grimes, D., Johnson, E., & Kaushansky, H. (2000). Predicting subscriber dissatisfaction and improving retention in the wireless telecommunication industry. IEEE Transactions on Neural Networks, 11, Rosset, S., Murad, U., Neumann, E., Idan, Y., & Gadi, P. (1999). Discovery of fraud rules for telecommunications challenges and solutions. Rosset, S., Neumann, E., Eick, U., & Vatnik (2003). Customer lifetime value models for decision support. Data Mining and Knowledge Discovery, 7(3), Sasisekharan, R., Seshadri, V., & Weiss, S (1996). Parallel data mining of Bayesian networks from telecommunication network data. Proceedings of the 14th International Parallel and Distributed Processing Symposium, IEEE Computer Society. Wei, C., & Chiu, I (2002). Turning telecommunications call details to churn prediction: A data mining approach. Expert Systems

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