Our objective is to identify interbank (i.e., non-collateralized) Identificando préstamos interbancarios, tasas y redes de acreencias a partir de datos transaccionales Resumen: Buscamos identificar los préstamos de fondos interbancarios (i.e., no colateralizados) a partir de información transaccional del sistema de pagos de alto valor por medio del método de Furfine. Con base en dichos préstamos, y sin recurrir a información reportada por las instituciones financieras, calculamos las tasas y los saldos interbancarios. El resultado del contraste de los préstamos identificados con aquellos reportados

The balance sheet is a snapshot that portraits the financial position of a firm at a specific point of time. Under the reasonable assumption that the financial position of a firm is unique and representative, we use a basic artificial neural network pattern recognition method on Colombian banks' 2000-2014 monthly 25-account balance sheet data to test whether it is possible to classify them with fair accuracy. Results demonstrate that the chosen method is able to classify out-of-sample banks by learning the main features of their balance sheets, and with great accuracy. Results confirm that balance sheets are unique and representative for each bank, and that an artificial neural network is capable of recognizing a bank by its financial accounts. Further developments fostered by our findings may contribute to enhancing financial authorities' supervision and oversight duties, especially in designing early-warning systems.

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A core goal of regulators and financial authorities is to understand how market prices convey information on the financial health of its participants. From this viewpoint we build an Early-Warning Indicators System (EWIS) that allows for identifying those financial institutions perceived as risky counterparts by the participants of the interbank market. We use micro-level data from bilateral overnight unsecured loans performed in the interbank market between January 2011 and December 2014. The EWIS identifies those participants that systematically pay high prices for liquidity in this market. We employ coverage tests to estimate EWIS' robustness and consistency. We find that financial institutions with an elevated frequency of signals tend to exhibit a net borrower liquidity position in the interbank market, hence suggesting they are facing recurrent liquidity needs. Those institutions also exhibit higher probability of insolvency measured by the Z-score indicator. Thus, our results support the existence of market discipline based on peer-monitoring. Overall, the EWIS may assist financial authorities in focusing their attention and resources on those financial institutions perceived by the market as those closer to distress.

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