Almost any organization generates a large amount of data about its activity during its operation, and a large array of such data is of a transactional type:
• Information about credit card payments.
• Information about purchased goods.
• Production journals containing equipment work logs.
• Energy consumption statistic.
Transaction data typically has large volumes and requires specific hardware, software, and algorithms for processing.
These complexities make the implementation of the transactional data processing within an organization challenging, resulting in either data deletion or lying idle and incurring additional storage costs instead of generating revenue.
Understanding the situation, our company developed a platform for automated transactional data processing for our partner organizations.
Benefits of our solution implementation:
The owner/shareholder of the partner company.
To receive additional profit due to better processing of accumulated data without significant investments in an infrastructure.
The Director of Big Data at the partner company.
Achieve a guaranteed business result quickly. Business cases implemented on the platform already work and bring profits in dozens of similar organizations.
Lay the foundation for new business cases.
The team of analysts.
Get the opportunity to focus not on solving infrastructure tasks, but on developing new data analysis algorithms based on factors generated by the platform and proven their significance in dozens of cases of organizations of similar specialization.
Scenarios:
Several examples of data and business cases already solved with the help of the Digital Human platform.
- Internet behavior (captured interest in a specific topic)
Data collected about internet behavior is typically sparse. The Digital Human platform has built-in ready-made models that take into account this specific internet data and enable more accurate risk assessment and customer interest determination. - Telephone calls/SMS (customer interest is proved by their actions)
Effective processing of call information allows for assessing credit risks, predicting the need for a specific product, and identifying numbers of fraudsters attempting to steal money from your customers. - Payment transactions (confirmed purchases)
Ready-made models based on payment transactions allow for a more qualitative assessment of credit risk, as well as understanding the customer’s need for a specific product (loan, credit card, investments, microloan). - Location tracking (physical movements)
Geo-data based predictive models allow predicting the next location of the client, allowing for targeted product offerings directly before the client reaches a specific location. Geo-information based factors also result in significant improvement in lead generation and credit risk assessment models. - Text data (behavior description)
Often performed transactions contain additional textual description (characteristics of MCC codes of payment transactions, POS data of retail network receipts).
High-quality text information processing requires the use of modern artificial intelligence algorithms and large computing capacities.
However, these costs are justified because textual descriptions allow for the construction of more accurate transaction category directories and significantly improve the quality of predictive models based on transaction data. - Graph data (interconnection between actions)
Looking at data from the perspective of graph characteristics allows for the discovery of new unexpected patterns (detection of criminal communities and money laundering schemes, finding opinion leaders, identifying the spread of information and infections). However, graph algorithms require the use of specific algorithms and large computational resources, which is not possible without specialized knowledge and specialized hardware and software infrastructure.
Privacy:
• The customer data does not leave the information perimeter of the partner company.
• The ordering company receives only triggers that make up the customer profile on an hourly basis.
• Digital Human platform facilitates the process of the ordering company receiving triggers without having information about which specific customer the triggered information belongs to.
Solutions:
• MedTech
• FinTech
• Retail
Our team:
Development of Digital Human platform is performed by the unique team of specialists with more than 15 years background in ML (machine learning) algorithms and AI (artificial intelligence) with experience of solving business cases in multiple industries (banking, telecommunication, retail, energy sector). A fusion of deep algorithmic knowledge combined with business expertise and understanding customer’s business need let us create a product which is unmatched on the market.
Email us to learn more:
info@digital-human.co.uk