High credit risk levels could impose systemic risk on the banking system which then leads into harming the overall economic condition of a country. Therefore, it is essential to discover whether the same theory is actually applicable in Indonesia. This research is aimed to find the determinants of credit risk in Indonesian banks. Specifically, bank-specific variables will be used as the determinants and to find out whether the bank ownership structure as one of the bank-specific variables influence the level of credit risk. Annual financial data selected from 2002 until 2013 will be used in this research.

Direct Benefits Transfer (DBT), as a new public policy, has been acknowledged by various segments of the society as an initiative for inclusiveness. It is a mechanism through which the target beneficiaries will get the subsidy amount in cash that shall be credited directly to their accounts by linking all the transactions to Aadhaar. DBT envisages a switch from the present subsidy scheme to transfer of benefits directly to Aadhaar seeded bank accounts of the beneficiaries. It is an earnest endeavour to streamline the subsidy mechanism in India. The scheme has potential to control much rooted corruption in public distribution system, by reducing the intermediate cost, by improving the efficiency of the delivery system and by getting rid of the misuse of funds. Its success, in fact depends upon institutional and infrastructural development in the rural and urban as well through which the scheme can be better implemented. DBT should be leak proof and free from other malpractices and irregularities and needs to be implemented effectively and should concede all kind of policy reforms in the decision making process. As a comprehensive socio-economic protection package, DBT will certainly fulfill the living expectation of the government and people specially the deprived and hapless through ‘Aap Ka Paisa Aap Ke Haath’.

These studies will analysis the big data of the twitter conversation in Indonesia. The data obtained from Twitter.com which gave many feature for analysis. The features like topic, trending topic, retweet, favourite, mention, mention, link, and also information that contain only 140 characters. The election of topic will classified from the trending topic data then categorizing through five kind of topic (Arkaitz Zubiaga, 2013). The trending topics can rapidly spread on the Twitter in immediately via hash tag. In every tweet that had tweeted by user have a content that make interest the follower to forward or retweet the tweet. Previous study analized for the user influence in Twitter via the million follower fallacy (Meeyoung Cha, 2010). Those analyses will compare the data between gossips, bad news, and good news. The expected result is the trending topic via hash tag on twitter can rapidly spread via retweet system and the interestingness of Twitter feature give impact towards the spread of the topic. The system of the spread will be seen from the constrain sub graph system. The results can used by any area to launch something on the Twitter in order to make the information can spread in rapidly and give easiness for everyone to search the information via hash tag.

Contemporaneously, Value at Risk (VaR) is one of the most important measures of risk which is percentile of the profit and loss distribution of a portfolio over a specified period. We could explore portfolio risk and loss created through quick movement of the economy by using dynamic VaR method. To analyze VaR of the Jan.2006 to Sep. 2014 corn and soybean spot prices in CBOT, we propose the application of stochastic volatility with Student-t errors (SV-t) model that maximizes expected returns subject to a Value-at-Risk constraint to depict the risk of heteroscedasticity and leptokurtic accuracy. We also propose the efficient and best way-- Markov Chain Monte Carlo (MCMC) simulation estimation method. Empirical results show all coefficient estimates including jump effect and leverage effect, and that the VaR value of soybean is larger than that of corn indicating more price volatility in soybean than corn with shocks in the emerging international commodity markets. Speculators as well as business operators might be able to earn risk premium or avoid risk loss by the operation of portfolio changes. However, both corn and soybean price VaR value are more than 5% indicating possible underestimates of returns from portfolio operations. It is suggested that more portfolio returns of soybean and corn futures market operation may be available.