Challenges We solve for fintech

ML processing on the cloud is no longer sufficient to navigate the challenges  faced by FinTech mobile apps

NimbleEdge’s HOME runs on-device Inference & Training for large fraud & personalization models, thus leading to great accuracy uplifts at 1/5th of the cost in running these models.

fintech challenge

ML processing on the Cloud is no longer a solution for FinTech mobile apps managing payment transfers, payment gateways, lending personal loans, investments and insurance.
The cloud ecosystem limits the potential of FinTech apps in providing a fast, reliable, and Hyper-Personalized user experience.

NimbleEdge comes into the picture to help you take your speed, reliability and user personalization metrics to new benchmarks with their Mobile Edge based solution called ‘HOME’. It has the capabilities to do on-device ML processing at a much bigger scale than cloud systems could imagine!

Use Case (applies to all the FinTech apps)

Fraud detection models that try to flag fraudulent transactions

payment challenge

Speed & Reliability issues with transactions in non-real time ML systems on the cloud limit personalization levels as it operates with stale data i.e. lagging data for AI/MLHuge Costs of running real-time ML systems on the Cloud

~$220 Billion

worth of transactions are unsuccessful due to fraud and false decline flaggings, in the USt.


High response times of a minimum of 500ms-1500ms for a complete transaction  77% of the customers believe that real-time individualization/personalization is key for a great customer experience

Personalization vs. Privacy

Be it real-time or non-real-time ML Cloud-systems, sending customer data to a far-located server creates risk with fraud, speed, and reliable completion of transactions. With respect to recommendations of services/products/rewards, they don't always get the user’s personal interests accurately due to limits in effectiveness of personalization algorithms as the data is not granular but a product of 12-24 hours old batch processing. Granular data can be leveraged with on-device (user’s mobile devices) model deployment and processing but it is not practically possible due to the operational complexities of Edge processing - scalability & resource handling of the mobile devices.

Fintech cloud
HOME Platform -
Bringing models and ML data processing to the mobile devices at an infinite scale

NimbleEdge’s HOME runs on-device Inference & Training for large fraud & personalization models, thus leading to great accuracy uplifts at 1/5th of the cost in running these models. It also runs multiple models on the Edge for even a billion transactions at a time. Hence, providing reliable, fast and hyper-personalized transaction experiences at significantly lower costs!

key takeaways

Complete travel bookings without leaving the app, saving huge amounts of retargeting advertising expenses

~10-20% improvement in the model’s performances lead to

Betterment in transaction success rate through hyper-personalized fraud detection

tick mark

~10-20% improvement in the model’s performances lead to

Highly individualized product/service recommendations, leading to high customer retention and NPS metrics


Huge Topline growth due to more successful transaction numbers!

Raising fund

<20ms - minimum 25x faster transaction response time that lead to uplifts in NPS

key take icon

~33-80% - massive cloud cost savings from the ML infrastructure


NimbleEdge Platform manages both orchestration and execution capabilities for on-device ML

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Edge pipelines with
on-the-fly updates
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Use Cases

Leverage the Intelligent Edge for Your Industry


Increase in models’ performances lead to a rise in Conversion carts with higher order size
Search & Display recommendation models for product discovery for new and repeat orders Personalized offers and pricing
The non Real-time/Batch ML processing doesn't serve highly fluctuating or impulsive customer interests. Organizations need real-time ML systems but it is impossible to implement and scale them on the cloud with even five times the average cloud cost.
Read Use Case


See uplifts in game retention metrics like gaming duration, completion, game cross-sells and LTV
Contest SelectionMatchmaking and Ranking Cross-contests recommendationPersonalized offers and pricing
As a result of cloud’s limited infrastructure in providing scalability with respect to ML model deployments and processing in real-time, gaming apps adopt non real-time/batch processing that negatively affects click-through rates, game duration, completion, cross-sells, and lifetime value of players.
Read Use Case

Travel & Stay

Increase in average booking value with new and repeat customers with higher NPS & savings in cost of acquisition
Travel & Stay
Search/Service recommendation models  + Personalized offers and pricing
NimbleEdge’s HOME runs real-time ML - Inference & Training - on-device, ensuring performance uplifts in Search/Service recommendation and Personalized offers/pricing models at 1/5th of the cost to run them on the cloud.
Read Use Case

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