Where security,
efficiency, and
innovation converge
to reshape banking

In the digital age, banking institutions face increasing challenges in ensuring security, compliance, and customer trust. Our solutions empower banks to enhance security measures, streamline operations, and improve the customer experience through innovative technologies. We provide cutting-edge solutions for signature compliance, ATM activity monitoring, counterfeit detection, and identity verification, leveraging NFTs and Blockchain technology. Automatically classify commercial documents and verify account ownership. Computer vision can replace the time-consuming task of scanning customer information and verifying their identity. It can even detect fraudulent documents and counterfeit currency. Applications, forms, identification, contracts, checks, credit cards, and tax returns. You can label this data yourself using Roboflow Annotate or outsource these tasks to your preferred annotation service.


Ensure compliance with regulations and prevent fraud through advanced signature verification technology.

Automate signature matching processes for efficient document authentication.

Monitor ATM transactions in real-time to detect suspicious activity and prevent fraud.

Improve security measures
and protect customer assets with proactive monitoring.

ATM Activity


Utilize advanced algorithms to identify counterfeit currency and prevent its circulation.

Enhance security measures at banks and ATMs to safeguard against financial losses.

Implement robust identity verification solutions using biometrics andblockchain

Ensure secure and seamless customer onboarding while complying with regulatory


Why Choose Us?

Real-world case studiesand testimonials

Unlocking the potential
of banking:

Shaeryl's Solution for Mitigating Credit Risk

for a Leading Credit Card Provider

Client Overview: Shaeryl, a leading software company specializing in data analytics and predictive modeling, was approached by a prominent credit card provider facing challenges with increased credit loss. The client sought a solution to mitigate credit risk by acquiring the right customers.

Problem Statement: The client, a major credit card provider, was grappling with rising credit losses. To maintain profitability and sustainability, they needed to identify customers with lower credit risk profiles and offer credit cards selectively.

Strategies Implemented by Shaeryl: Shaeryl devised a multi-faceted approach to address the client's challenges:

Data Preparation:   Shaeryl meticulously prepared the data by leveraging demographic information and credit bureau data. They utilized techniques such as Information Value, Weight of Evidence, and Data Balancing using Synthetic Minority Over-sampling Technique (SMOTE) to ensure the dataset's quality and balance.

Exploratory Data Analysis (EDA):  Through comprehensive univariate and bivariate analyses, Shaeryl gained deeper insights into the relationships between different variables and identified key patterns and trends in the data.

Modeling Techniques: Shaeryl employed various modeling techniques to develop robust predictive models. This involved outlier treatment, data scaling to standardize the features, data splitting for training and testing, and data sampling to handle class imbalance.

Models Built: Leveraging advanced analytics, Shaeryl constructed several predictive models, including Logistic Regression, Decision Tree, and Random Forest, to forecast credit risk accurately.

Solution Implemented by Shaeryl:

Data Preparation: By meticulously preparing the data and employing advanced techniques, Shaeryl ensured the dataset's quality and balance, laying a solid foundation for subsequent analyses.

Exploratory Data Analysis: Through in-depth EDA, Shaeryl unearthed valuable insights into the data, enabling them to understand the underlying patterns and relationships critical for predictive modeling.

Model Development:  Shaeryl developed robust predictive models using state-of-the-art algorithms, incorporating best practices in outlier treatment, data scaling, and sampling to enhance model performance and reliability.

Business Outcome:

Credit Loss Reduction:** With the implementation of Shaeryl's predictive models, the client achieved a notable 3 percent reduction in credit losses, significantly mitigating financial risk.

Financial Gain:  Shaeryl's solution yielded substantial financial benefits for the client, with a remarkable 313.72 percent net financial gain realized after deploying the predictive models. This gain underscored the efficacy and profitability of Shaeryl's data-driven approach in mitigating credit risk and enhancing overall business performance.

In conclusion, Shaeryl's tailored solution empowered the leading credit card provider to make informed decisions, mitigate credit risk, and drive significant financial gains, underscoring the transformative impact of data analytics and predictive modeling in the financial services industry.


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