Unveiling Tenali: A comprehensive exploration of CredenTek’s AI-fintech platform

CredenTek recently ventured into the realm of artificial intelligence and machine learning


A Staff Reporter

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Pravin Gupta, CEO of CredenTek Software. — Supplied photo
Pravin Gupta, CEO of CredenTek Software. — Supplied photo

Published: Thu 16 Nov 2023, 4:13 PM

In an exclusive interview with Khaleej Times, Pravin Gupta, the dynamic CEO of CredenTek Software, offered profound insights into the recently launched AI-fintech platform, Tenali. CredenTek, a stalwart with 15 years of experience in banking technologies and a distinguished clientele of tier-1 banks, has boldly ventured into the realm of artificial intelligence and machine learning with the state-of-the-art Tenali platform.

What is the inspiration behind the development of Tenali and how does it align with CredenTek’s vision?

Pravin Gupta: CredenTek has been unwavering in its commitment to pushing the boundaries of innovation in the banking technology sector. The pivotal moment came in 2012 with our foray into Corporate and MSME Mobile Banking, laying the foundation for our ongoing commitment to staying at the forefront of technological advancements in the financial sector. With the rapid evolution of AI and ML technologies, we saw an immense opportunity to enhance the efficiency and intelligence of financial services. This mindset has been integral to our approach in developing Tenali, where our aim is not just to meet but to exceed the evolving demands of the BFSI domain.

Tenali incorporates BERT, Hugging Face Transformers, and NER. Can you elucidate on how these technologies contribute to the platform’s capabilities?

BERT, developed by Google, serves as the bedrock for natural language processing within Tenali. This allows us to analyse and comprehend the contextual nuances of financial data, ensuring more accurate and meaningful insights. Hugging Face Transformers, in turn, empower us to seamlessly implement advanced machine learning models, providing a flexible and efficient framework for model deployment and management. The integration of Named Entity Recognition (NER) enhances data extraction capabilities, facilitating the identification of key entities in the intricate landscape of the financial domain.

How does Tenali differentiate itself from other AI-fintech platforms in the market?

Tenali stands out due to its deep integration of state-of-the-art technologies, offering a comprehensive solution tailored specifically to the BFSI sector. The platform’s distinctive ability to understand complex financial language, coupled with its adaptability through Hugging Face Transformers, sets it apart. Furthermore, our extensive experience in banking technologies positions us uniquely to address industry-specific challenges with a nuanced approach. Tenali has undergone extensive training on financial data, ensuring its efficacy in real-world applications.

Could you provide examples of real-world applications where Tenali has demonstrated significant impact?

One of the core strengths of Tenali lies in its ability to revolutionise credit decisioning through the integration of Machine Learning (ML) . The platform leverages sophisticated ML algorithms to analyse vast datasets, providing financial institutions with a more nuanced and accurate assessment of creditworthiness. By incorporating factors beyond traditional credit scores, Tenali ensures a comprehensive evaluation, enabling our clients to make more informed and precise credit decisions.

In the area of Natural User Interface (NUI), Tenali has been designed with a user-centric approach. We understand the importance of user experience in the BFSI sector, and NUI plays a pivotal role in enhancing accessibility and usability. Tenali’s adoption of NUI means that users can interact with the platform in a more intuitive and natural manner, significantly reducing the learning curve. This user-friendly interface is crucial for financial institutions looking to seamlessly integrate advanced technologies like ML into their operations without sacrificing ease of use.

How has the response been from your existing clientele?

The response has been overwhelmingly positive. Banks, with their stringent requirements and high standards, appreciate the seamless integration of Tenali into their existing systems. The platform’s ability to enhance operational efficiency and provide valuable insights aligns well with the priorities of these institutions.

Could you provide an overview of CredenTek’s diverse stack of solutions surrounding digital onboarding, reconciliation, remittance, host-to-host secure data transfer, lending solutions, internet banking, and mobile banking?

CredenTek, over the years, has established itself as a comprehensive IT solutions provider for the banking industry. Our digital onboarding solutions streamline the customer onboarding process, ensuring a seamless and user-friendly experience. The ML based reconciliation tools are designed to enhance accuracy and efficiency in financial data management. Remittance solutions facilitate secure and swift cross-border transactions, while our host-to-host secure data transfer ensures the integrity and confidentiality of sensitive financial information. Lending solutions cater to the diverse needs of financial institutions, and our internet and mobile banking solutions provide a robust digital interface for customers, ensuring accessibility and convenience.

Could you elaborate on how the company has incorporated ML-based techniques in its reconciliation platform, Rhinocon-BRAIN?

Real-time reconciliation is crucial in the dynamic landscape of financial operations, and at CredenTek, we recognise the significance of leveraging ML to enhance this process. Rhinocon-BRAIN, our reconciliation platform, is a testament to our commitment to innovation. By incorporating ML-based techniques, we aim to revolutionise how financial data reconciliation is conducted.

The key advantage of Rhinocon-BRAIN lies in its ability to adapt and learn from historical data patterns. Traditional reconciliation methods often face challenges in handling large datasets and identifying complex discrepancies. Rhinocon-BRAIN overcomes these challenges by employing machine learning algorithms that analyse historical transaction data, allowing the platform to automatically recognise and reconcile patterns in real-time.

How does the incorporation of ML-based techniques in Rhinocon-BRAIN contribute to improving efficiency and accuracy in financial data reconciliation?

The incorporation of ML in Rhinocon-BRAIN brings about a paradigm shift in how reconciliation is approached. The platform continuously learns from historical data, adapting to evolving patterns and anomalies. This results in a more intelligent and automated reconciliation process, significantly improving efficiency by reducing the need for manual intervention.

Moreover, ML allows Rhinocon-BRAIN to identify and rectify errors in real-time, preventing potential financial discrepancies from escalating. The platform’s ability to handle large volumes of data with speed and precision not only improves the overall efficiency of the reconciliation process but also enhances the accuracy of financial data, a critical aspect in the realm of banking and finance.

Looking ahead, what developments or expansions do you envision for Tenali in the coming years?

We are committed to continuously evolve Tenali to meet the ever-changing landscape of the BFSI domain. Future developments will focus on enhancing the platform’s capabilities through ongoing research and development in AI and ML. We also plan to expand our intelligent banking offerings to cater to a broader range of financial services, ensuring that Tenali remains at the forefront of innovation in the industry.

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