Automated credit assessment framework using ETL process and machine learning

In the current business scenario, real-time analysis of enterprise data through Business Intelligence (BI) is crucial for supporting operational activities and taking any strategic decision. The automated ETL (extraction, transformation, and load) process ensures data ingestion into the data warehouse in near real-time, and insights are generated through the BI process based on real-time data. In this paper, we have concentrated on automated credit risk assessment in the financial domain based on the machine learning approach. The machine learning-based classification techniques can furnish a self-regulating process to categorize data. Establishing an automated credit decision-making system helps the lending institution to manage the risks, increase operational efficiency and comply with regulators. In this paper, an empirical approach is taken for credit risk assessment using logistic regression and neural network classification method in compliance with Basel II standards. Here, Basel II standards are adopted to calculate the expected loss. The required data integration for building machine learning models is done through an automated ETL process. We have concluded this research work by evaluating this new methodology for credit risk assessment

Sequence and expression variations in 23 genes involved in mitochondrial and non-mitochondrial apoptotic pathways and risk of oral leukoplakia and cancer

Oral cancer is usually preceded by pre-cancerous lesion and related to tobacco abuse. Tobacco carcinogens damage DNA and cells harboring such damaged DNA normally undergo apoptotic death, but cancer cells are exceptionally resistant to apoptosis. Here we studied association between sequence and expression variations in apoptotic pathway genes and risk of oral cancer and precancer. Ninety nine tag SNPs in 23 genes, involved in mitochondrial and non-mitochondrial apoptotic pathways,were genotyped in 525 cancer and 253 leukoplakia patients and 538 healthy controls using Illumina Golden Gate assay. Six SNPs (rs1473418 at BCL2; rs1950252 at BCL2L2; rs8190315 at BID; rs511044 at CASP1; rs2227310 at CASP7 and rs13010627 at CASP10) significantlymodified risk of oral cancer but SNPs only at BCL2, CASP1and CASP10 modulated risk of leukoplakia. Combination of SNPs showed a steep increase in risk of cancerwith increase in “effective” number of risk alleles. In silico analysis of published data set and our unpublished RNAseq data suggest that change in expression of BID and CASP7 may have affected risk of cancer. In conclusion, three SNPs, rs1473418 in BCL2, rs1950252 in BCL2L2 and rs511044 in CASP1, are being implicated for the first time in oral cancer. Since SNPs at BCL2, CASP1 and CASP10modulated risk of both leukoplakia and cancer, so, they should be studied inmore details for possible biomarkers in transition of leukoplakia to cancer. This study also implies importance of mitochondrial apoptotic pathway gene (such as BCL2) in progression of leukoplakia to oral cancer