Bahaa Abdul Hadi discusses the evolution of data handling in the fintech sector, emphasizing the urgent need for financial institutions to update their data frameworks to stay competitive. He outlines a structured approach for managing data, from extraction and transformation to analysis and visualization, highlighting the role of modern technologies like AI. The document stresses that early adoption of innovative data solutions will lead to greater success for businesses in the financial industry.

1. Data Evolution in a FinTech - Bahaa Abdul
Hadi
Bahaa Abdul Hussein is a Fintech expert and shares his experiences with his
audience through his blogs.
The economy and competition in the financial industry have created a global context
that is nudging banks to create a new data frame that is in tune with new needs.
Financial institutions must refurbish their reporting mechanisms, while balancing
cost, quality and production.
Every business owner has opportunities to create data that is valuable. This data can
be used by optimizing your operations and defining strategies that will take your
business to new heights.
What Must be Done by Businesses?
To achieve operational excellence without a burden on the production team, ease of
data handling is paramount. It could become cost banks dearly when they use old
techniques to handle data.
Since risk management is an extensive, complex process, we’ll talk about the
multitude of problems during this journey and look at how new technology can make
it easier.
Going Step-by-Step
● Stock-take and Productivity
The first challenging step in data manipulation is in extraction of data bundles from
data pools or legacy systems. Large data volumes may be collected, and storing it in
absence of a proper strategy to manage data can be costly.
● Transformation
After acquisition of data, aggregation of data is required. It’s important to note that
here we need to rectify or purify all the faulty datasets. At the same time, we need to
enrich the granularity of the data. Other adjustments which are not automated are a
daily concern for reporting teams and consume most of their production time.
● Calculate
This stage of data analysis usually relies heavily on analysis and testing. It involves
calculating key metrics. These calculations can interfere with the process of

2. production, leaving users with metrics that might not be sufficient for investigation of
data.
● Certify and Analyse
After performing multiple analyses and investigating them, the next step is to verify
the accuracy of the metrics that are calculated. Using Artificial Intelligence for this
step will significantly increase efficiency and reduce the time required in getting this
step of the process done.
● Display and Sharing
And finally, we exhibit all the dashboards to different stakeholders. The teams that
produce this content need to be supported by an automated data visualization layer.
However, this automation must be flexible for the analyst to focus on other tasks
with more value. Easily displaying information through a single dashboard is
essential for information-sharing and support.
Final Words:
Financial institutions are showing a increased awareness of the need for modern
data solutions. It’s clear that evolution is happening. And those who adopt it early will
be more successful as compared to those who wait. Thank you for your interest in
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