The Good Tech Companies - How Cloud Computing Is Revolutionizing Customer Verification in Banking
Episode Date: November 5, 2025This story was originally published on HackerNoon at: https://hackernoon.com/how-cloud-computing-is-revolutionizing-customer-verification-in-banking. Data engineer Vijay...alakshmi Alice Singavarapu modernized customer verification with AWS automation, cutting costs and speeding compliance in major banks. Check more stories related to cloud at: https://hackernoon.com/c/cloud. You can also check exclusive content about #cloud-computing-in-banking, #aws-ec2-compliance-system, #vijayalakshmi-singavarapu, #know-your-customer-(kyc), #customer-verification, #financial-compliance, #banking-automation-solutions, #good-company, and more. This story was written by: @jonstojanjournalist. Learn more about this writer by checking @jonstojanjournalist's about page, and for more stories, please visit hackernoon.com. Vijayalakshmi Alice Singavarapu transformed customer verification at a major U.S. bank by building an AWS EC2–based automation system that processes 13M records yearly. Her cloud-powered solution cut verification times from days to minutes, improved accuracy, reduced fraud risk, and set a new standard for compliance automation in banking.
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How Cloud Computing is revolutionizing customer verification in banking, by John Stoy and
journalist. Banks process 50,000 new customer applications every week. Each application needs to be
checked against government databases. Staff must verify social security numbers, addresses,
and birth dates by hand. Most banks still do this work manually. The process takes days and
costs millions. American banks now spend $61 billion each year on compliance, according to a
2024 study. The USA Patriot Act requires banks to verify every customer through know-your-customer
and anti-money laundering checks. Banks spend more money each year, but still have backlogs,
angry customers, and government fines. New rules make things harder. Regulators updated
requirements in 2024 and want faster responses. Banks now face tighter deadlines while handling
more applications than before. The old manual system cannot keep up. Staff spend hours checking
customer information that arrives from different places. Online forms have missing details.
Phone applications have typos. Branch paperwork gets filed incorrectly. Compliance teams work overtime
reviewing each case. Customers wait weeks for account approval. Tired employees miss important
warning signs. Vijayalakshmi Alice Singavrapu faced these problems every day as a data
engineer at a major bank. She managed the customer identification program that processed about
13 million customer records each year. Every record needed to be checked against multiple databases
to follow federal rules. The manual system was failing under the load. She knew how to build
systems that handled huge amounts of information. She saw that customer verification could work
faster and better with automation. Instead of accepting slow manual reviews, she rebuilt the whole
system. Building automated verification systems, she created her solution using Amazon EC2 servers
that could handle thousands of customer records at once. Her system pulled customer data from many
sources, fixed inconsistencies, and ran everything through checks that compared details
against government and internal databases. She set up Oz EC2 computers for batch processing
jobs. The system could handle the bank's entire customer database while running checks that would
take human staff weeks to finish. She wrote rule-based programs that found problems in customer
information. The system caught mismatched social security numbers, wrong birth dates, bad addresses,
and fake document numbers. Her automated system put every customer record into three groups.
Good accounts that passed all checks went straight to approval. Records with small problems went
to a review list for staff attention. Bad records that failed checks got flagged right away for
investigation. She also built reports that showed compliance officers exactly what the system found.
The reports listed data problems, success rates, and accounts that needed follow-up work.
This gave business teams clear records for government audits and helped them spot patterns
in application issues. The system we built changed everything about customer verification,
Vigayalakshmi Alice Singavarapu says. Compliance teams stopped spending all-day checking routine cases
and could work on complex situations that needed human judgment.
Her automation handled basic verification tasks that used to take up most of the compliance staff's time.
Her system processed normal applications in minutes instead of days while flagging truly
suspicious cases for immediate review.
Improving accuracy while cutting costs.
Her customer identification program delivered results beyond just speed gains.
Faster processing meant customers got account approvals in hours instead of weeks.
The automated checks caught more problems than manual reviews, cutting fraud risk and rule violations.
She worked with compliance officers, fraud investigators, data teams, and security staff to make
sure her system met both technical and regulatory needs.
This teamwork created a new way to implement compliance technology that balanced automation
with human oversight. Her system solved banking's biggest compliance problem.
It maintained regulatory standards while processing applications fast enough to satisfy customers.
She proved that automation could improve compliance quality rather than herded.
Automated systems gave more consistent verification than human reviewers while cutting processing time dramatically.
The work showed how data engineering skills could tackle complex regulatory problems while
improving customer service and operational efficiency.
The teamwork between technical and compliance staff became a model for other regulatory technology projects.
When you process 13 million customer records, you need consistency, Vigayalakshmi Alice
Singaporeapu explains. Automated systems don't make mistakes from being tired, don't miss
important details, and treat every application the same way. The success came from her technical
approach combined with careful collaboration across different bank departments. Her system
handled massive data volumes while maintaining the accuracy and documentation that regulators require.
Changing how banks handle compliance, her customer identification program shows changes happening
across financial services. Banks that still use manual compliance processes face growing pressure
from regulators and customers who want faster service. The methods she used are becoming standard
tools for modern compliance work. Cloud-based batch processing, rule-based validation, and automated
sorting now appear in compliance systems at banks nationwide. Her use of Oz EC2 computers showed
how cloud technology could handle massive customer databases without breaking budgets. The teamwork approach
she took with compliance staff has become the standard for successful regulatory technology projects.
Banks that make sure automated systems meet both technical performance goals and regulatory requirements
get better compliance results while improving operational measures. Her customer verification methods
now spread to other compliance areas, including transaction monitoring and regulatory reporting.
Banks that adopt these automated approaches gain advantages through faster processing,
lower costs, and stronger regulatory compliance. Industry data shows compliance costs increased
for 98% of financial institutions in 2023, making automation solutions like hers more valuable.
Banks that embrace data engineering approaches to compliance challenges turn regulatory requirements
into competitive advantages that improve both regulatory outcomes and customer satisfaction.
Projects like Vijayalakshmi represent a shift in how banks handle compliance work. Smart institutions
used technical expertise to transform compliance from an expensive burden into a competitive edge.
Thank you for listening to this Hackernoon story, read by artificial intelligence.
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