Machine learning, a part of AI, is rapidly integrated into financial companies and in today’s technologies have accelerated with Machine learning which analyses historical data, behaviors to predict patterns and make decisions.

Functions such as Key Analytical data sets Designed, development are now automated for banks/financial institutions. To leverage machine learning and predictive analytics to offer their customers a much more personalised experience, recommend new products and provide loyalty points or offers. or types of reports, of each different verticals to predict the data of merchants, customers, and behaviour patterns and needs of the items which help in forecasting the business of respective of our customers.

Historical and structured data in the financial services, making use of it as a perfect playing field for machine learning technologies.

Technology stacks for Machine Learning:

  • Databases: Snowflake, BigData, Oracle, MS SQL Server

  • Language: Custom Python, Java, Power BI, SSIS, SSRS

  • ETL Tools: Tableau, Talend, Oracle Based ETL process

  • Regression & classification algorithms: Logistic Regression, Decision Trees, Random Forest, Dimension Reduction, K-Fold Cross validation, XGBoost, Handling Imbalanced dataset, Hyperparameter tuning Cross Validation, Gradient Boosting Machine(GBM) Algorithm, Ada Boost Classifier Algorithm

  • Neural network & computer vision: Tensor flow, Keras & openCV

  • Build tools: Oracle Data modeller, SQL Developer, Linux Shell Scripting, AWS S3 Buckets, SnowSQL, DBeaver

Use cases leveraged in Payments sector

We practise delivery methodology with a blend of onsite and offshore resources exercising a full-bodied communication grid. Customers can successfully accomplish their IT budget and balance their project priorities by indicating the crucial services whenever needed, and for as long as essential.

Service Description

Our Database Services guarantees that customer data and databases are safeguarded and supervised by establishing High-Availability, backup and recovery measures, delivering secured database environments, and examining database performance as well. Database software support is available for all popular databases and releases.

Services provided include

  • Technology stack selection

  • Requirement analysis & design of databases

  • Initial database software installation in various environments

  • Data conversion/migration execution

  • Database upgrades/patching

  • Database security maintenance

  • Configuration & verification as required

  • Database High-Availability (business continuity), monitoring & management

  • Performance tuning & monitoring

  • Designing, executing Backup & Restore policies

  • Systematic treatment of Data at rest & Obsolete

  • Administration & monitoring

  • Database restoration, as required

  • Administration of various DBA functions

  • Respond, resolve issues related to alerts & customer requirements

Machine learning functions such as fraud detection and credit scoring and Payment Devices are some part which general banks/financial institutions leverages to their customers, but the financial services tend to encounter enormous volumes of data relating to daily transactions, bills, payments, vendors, and customers, which are perfect for machine learning.

In recent days, many leading financial services companies are incorporating machine learning into their operations, resulting in a better-streamlined process, reduced risks, and better-optimize.

Build a best fit model, based on previous payment history Loan delinquency prediction using machine learning algorithms using Python, Statistics, Logistic Regression, Cross Validation, Gradient Boosting Machine (GBM) Algorithm, Random Forest, Ada Boost Classifier Algorithm

We engage with our customers for DB related services as per their needs and at the stage they need our services for short and long term.

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