Revolutionizing Banking Development with Low-Code Solutions
Build, innovate, and deploy banking services effortlessly
At 3beacons, we believe that financial innovation should be accessible to everyone. Our mission is to empower banks and FinTech companies with a low-code platform that simplifies the development of banking services, enabling rapid innovation and transformation in the financial sector.
3beacons is a team of passionate technologists and banking experts dedicated to reshaping the financial landscape. With years of experience in both technology and finance, we understand the challenges faced by traditional banking institutions and emerging FinTechs alike. Our diverse team brings a wealth of knowledge and creativity to the table, driving us to create solutions that meet the evolving needs of the industry.
3beacons empowers financial institutions to rapidly innovate with our user-friendly low-code platform, enabling seamless integration and robust security. Transform your banking services effortlessly and stay ahead in a fast-evolving industry!
Our future-ready platforms evolve with emerging technologies and customer expectations. Built on a foundation of continuous innovation, they feature intelligent algorithms that dynamically adjust to your evolving needs.
3B’s intelligent algorithms empower seamless, proactive, and customized banking operations. Intelligent automation also optimizes processes for faster, more efficient service delivery to clients.
3B’s intelligent automated processes reduce manual tasks and improve operational efficiency. Automation enhances accuracy and consistency across all banking operations.
3B’s Currency Chest Automation Solution streamlines cash management with precision and speed. Intelligent systems optimize cash allocation based on demand and usage patterns. Robust automation improves efficiency, security, and compliance in currency chest operations.
By Dr. Abhijeet Malge, Dean – MIT
Generative Adversarial Networks (GANs) have redefined image synthesis, powering applications from medical imaging to computer vision. Traditional models such as Wasserstein GANs and Progressive GANs improved stability and resolution but often fell short in preserving fine details and structural consistency.
A new approach, the ResNet-based UNet++ GAN, combines the strengths of residual learning with the nested skip connections of UNet++. Residual blocks enhance training stability and feature reuse, while the UNet++ architecture captures multi-scale contextual information through its dense skip pathways. This hybrid design enables the generator to retain intricate spatial details, producing sharper and more realistic images.
Evaluations across datasets like CelebA and CIFAR-10 show that the ResNet-based UNet++ GAN surpasses earlier architectures in both visual quality and accuracy. By addressing limitations of existing GANs, this model paves the way for robust, high-fidelity image generation, making it highly relevant for domains requiring precision and realism.