Millions of people in developing countries have been given access to formal financial services through microfinance programmes. Nevertheless, millions of potential clients still remain unserved and the demand for financial services far exceeds the currently available supply. Given significant capital constraints, expansion of microfinance programs remains a formidable challenge facing the microfinance industry. Moreover, it is observed that microfinance organizations have had various degrees of sustainability. One such sustainability is the financial sustainability. Financial sustainability has been defined by various researchers differently. As such, there is no clear-cut definition of the word financial sustainability. The MIX Market and various other agencies like ACCION, Women's World Banking etc. define the term financial sustainability, but this term has not been defined lucidly. Therefore, this paper attempts to propose a more comprehensive and representative model for financial sustainability. This model of financial sustainability gives due weightage to some of the critical financial indicators like Portfolio at Risk, Loan loss, Borrowers per Credit Officers etc.

Banking industry has gone through one of the worst crisis in recent times, and is still recovering from the after-shocks. However, there were a lot of learnings that banks would have taken away from this crisis. One of them is the need for a robust risk management system. The crisis dealt a blow to the banking system, catching them off guard when it came to foreseeing the risk. Banks, in the credit card business, face financial risk in the form of both credit risk and fraud risk. Sharma and Agarwal (2013) proposed a model for predicting the credit risk from the merchants. This paper builds upon their technique to predict the fraud risk posed by the merchants to the banks. Fraud risk is an important aspect of risk management systems, particularly in the credit space. The uncertainty surrounding the receipt of paybacks calls for designing robust risk prediction models. Fraud risk is very different from credit risk because fraud risk does not follow a pattern. It happens suddenly, and may not always have a trend before it happens. This creates a need for separate model for fraud risk prediction. This paper develops a fraud risk prediction model that uses logistic regression technique, deployed using SAS. The setup of the study is the merchant-bank relationship in the credit card industry. The model developed in this paper triggers on a transaction level, and assigns a ‘probability score of default (PF) to each merchant for a possible fraud risk whenever a transaction is done at the merchant. Such a score warns the management in advance of probable future losses on merchant accounts. Banks can rank order merchants based on their PF score, and instead of working on the entire merchant portfolio, they can focus on the relatively riskier set of merchants. The PF model is validated by comparing the actual defaults with those predicted by the model and a good alignment is found between the two. The results show that the model can capture 62 percent frauds in the first decile when the transactions are sorted by the probability of fraud computed by the model.

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Fraudulent activities in financial fields are continuously rising. The fraud patterns tend to vary with time, and no consistency can be observed in this regard. The incorporation of new technology by fraudsters is the reason for the execution of online fraud transactions. Given the volatility of the fraud patterns, a good fraud detection model must be able to evolve and update itself to the changing patterns. Thus we aim in this paper to analyze the fraud cases that are unable to be detected based on supervised learning or previous history, create an Auto-encoder model based on deep learning and Compare and assess the performance of the model based on data from different parts of the world and check for the demographic diversity of fraud patterns thereby inferring that the data from which part of the world the model fits the best. The proposed algorithm, deep learning based on the auto-encoder (AE) network is an unsupervised learning algorithm that utilizes backpropagation by setting the inputs and outputs identical. In this research, the Tensorflow package from Google has been employed to implement AE by using deep learning. The accuracy, precision, recall, F1 score and area under the curve(AUC) are all executed to assess the performance of the model.

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