According to recent studies, 94% of banking firms are unable to deliver on personalization promise to their customers and only 30% of banks have effectively matched their analytics efforts with their business goals. Meanwhile, only 20% of banking institutions are prepared to take the necessary actions to become truly customer-centric.
Here are the top 5 reasons why traditional analytics fail by design:
- Data cleaning and pre-processing is a huge bottleneck to perform analytics
- Inefficient, legacy infrastructure in the banking sector makes it near impossible to match the pace of data origination
- Enterprise data needs to be enriched with banking literature, reports and tribal knowledge generated by enterprises
- Most traditional analytics tools have little or no capability to analyze unstructured banking data
- Traditional analytics platforms pigeon-hole data into silos, and cannot preserve lineage and relationships
MECBot: Real-Time Augmented Data Management at Scale
Our flagship product, MECBot, is a leading Augmented Data Management Platform for Real-Time Analytics at Scale. MECBot puts your business first by adopting the Business Domain Entity-Model approach without any dependency on the underlying databases or the structure of the data. It comes bundled with a self-service, intuitive interface and takes care of all your data management and analytics requirements in a centralized manner, including scalable deployment.
With MECBot, a business model can be directly created by CXOs, Data Scientists, Data Analysts or all of them collaboratively MECBot directly pulls the data from the configured sources and maps it to the specified Business Domain-Entity Model. Data engineers can configure MECBot with available sources and provide the details on interlinkages. It provides advanced analytics modules that work out-of-the-box and allows users to build various models such as customer loyalty, churn, and segmentation, without having to move the data around. MECBot allows you to create a flattened data view for the chosen entities without writing any complex SQL joins. The application can reuse existing views as well, which means customers can get started on the same day instead of waiting for months to get it up-and-running.
There is no dependency on the data team in terms of their skill set to deploy a solution or requirement to scale the data team as per demand. This aspect reduces the enterprise’s technical debt drastically and allows them to scale-up or down dynamically based on the load on the system. Our out-of-the-box exploratory analysis and advanced analytics modules are built on top of a smart enterprise graph that captures your business domain in the most comprehensive manner. Our built-in free-form search makes coding redundant – you can extract self-service insights from MECBot on demand by posting queries in simple English. The following image summarizes how MECBot fosters augmented data management for your enterprise:
Formcept’s Collaboration with Intel:
Our collaboration with Intel is aligned with our vision to democratize analytics by leveraging the partnership at all levels – infrastructure, technology, research, and networking.
MECBOT has various in-built models to structure unstructured data like images, videos, textual data, etc. Some of these models are built using TensorFlow while some are built using Python; regardless, all of them are compute-intensive. As such, using software optimizations like the Intel distribution for Python and the Intel optimization for TensorFlow ensures that we get optimal performance.
Working with Intel’s engineering teams through the Intel® AI Builders program has helped improve the efficiency and speed of MECBot, enabling it to complete complex computations faster than before. Using Intel AI tools and technologies, our models run more efficiently compared to running them without the optimized software packages on the same hardware. We have also gained deeper insights on end-user challenges in the AI/analytics domain in general and how Intel is solving some of these critical issues through this ecosystem program. The primary benefit we have seen is a boost in performance and efficiency. Given the success of our joint enabling efforts, we intend to optimize other solutions on Intel architecture so that our customers can derive substantial benefits of higher efficiency.
Case Study: Optimizing Cash Management in ATMs with MECBoT
A Fortune 500 financial software services company in India was approached by the country’s banking authorities to address the pressing challenge of ATM machines running out of cash. India has an ATM density of 180 per million of the country’s population, which is significantly lower than many other countries like China (630 ATMs per million of its population), Brazil (810 ATMs per million of its population) and Japan (1,070 ATMs per million of its population). The scenario is further aggravated by the fact that India is predominantly a cash economy.
The challenge for this client was two-fold: (1) large scale data management, wherein varied and disparate forms of data needed to be processed, cleaned, unified and analyzed, and (2) predictive analytics to determine which ATMs might be running low on cash and re-filling them. Both issues were quickly addressed by MECBot. Data processing and management was handled using the patented data-folding technique. ATM switch data was made available to MECBot once a day, and used to predict how much cash would be left in an ATM. If the cash in an ATM fell below the threshold amount, MECBot automatically alerted the respective user(s) in real-time to refill the cash. Furthermore, the platform also deployed predictive analytics to recommend the most suitable currency denominations and their quantities based on historical usage patterns, which was also sent as an alert to the user(s).
In partnership with MECBot, a global frontrunner in financial services and technology consulting was able to minimize cash shortage in ATMs, optimize currency denominations based on the usage patterns of the population, and drastically reduce the frequency of roundtrips required between banks and ATMs to ensure round-the-clock cash availability. With this solution, banks have reduced penalties for empty ATMs and minimized the service fees paid to cash transport companies.
Our partnership with Intel has given us a great platform to leverage the AI innovation ecosystem across the globe, as well as Intel’s own breakthrough technologies. Our vision is to democratize business intelligence and make it easily accessible to all. Disruption is key in AI, and this is where we find value in our work with Intel to bring more effective AI-driven data analytics solutions to market.
MECBot solves the toughest challenges in Banking and several other industries like Insurance, Legal, Retail, Healthcare, EVSE, etc. with augmented analytics. Interested to know how MECBot’s Augmented Data Management platform can help your business? Please visit https://www.mecbot.ai/use-cases/