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PUBLISHED DATE: - 02-10-2024
https://doi.org/10.37547/tajet/Volume06Issue10-02
PAGE NO.: - 9-16
ADDRESSING TAX COMPLIANCE ISSUES FOR
LOAN-BASED PAYMENT TYPES:
DEVELOPMENT OF THE TAX BUFFER
MECHANISM AND ITS USE IN THE FINTECH
INDUSTRY
Panguluri Naga Rishyendar
Software Engineering Manager, Discover Financial Services, Leander, TX,
USA
INTRODUCTION
The modern fintech industry is rapidly evolving,
facing numerous challenges related to tax
regulation and shifting economic conditions. One
of the most complex tasks is managing tax
obligations in the context of fixed loan amounts,
particularly in situations where tax rates fluctuate
across different jurisdictions. This creates
significant
difficulties
for
both
financial
institutions and their clients, necessitating
innovative solutions that can ensure flexibility and
accuracy in tax calculations.
The relevance of this topic is driven by the growing
role of fintech companies in the global economy
and the need for effective tax risk management. In
this context, the development of mechanisms like
the Tax Buffer becomes a key element in ensuring
the stability and reliability of financial operations.
The purpose of this paper is to explore the
development and application of the Tax Buffer
mechanism in the fintech industry and to analyze
its impact on optimizing tax obligations and
minimizing risks associated with changes in tax
legislation.
RESEARCH ARTICLE
Open Access
Abstract
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1. Operation Principle and Structure of the Tax
Buffer Mechanism
To begin the discussion on this topic, it is
important to first explore the theoretical aspects
related to the activities of fintech companies. The
term "financial technology," or "fintech," refers to
the use of modern technologies to optimize
financial operations and improve the accessibility
of financial services. Fintech products and
solutions are aimed at simplifying financial
management for both businesses and individuals,
made possible by integrating specialized software
and algorithms that operate on various devices,
including computers and mobile phones.
One of the key characteristics of most fintech
companies is their drive to compete with
traditional financial institutions by offering more
flexible and accessible services. These companies
typically target audiences underserved by
conventional financial organizations. For example,
Affirm provides quick loans for online purchases,
eliminating the need for customers to use credit
cards. Although interest rates on such loans can be
high, the company offers credit options to people
with limited credit history.
Other examples of fintech include companies like
Better Mortgage, which offers online mortgage
services, and Tala, which provides microloans in
developing countries based on data analysis from
users' mobile devices. These companies aim to
offer more convenient and accessible financial
products compared to traditional banking
solutions. The fintech sector plays a significant role
in accelerating the digitization of financial services
and transforming traditional approaches to
managing financial operations [1].
Currently, there are significant challenges in tax
legislation, driven by the varying approaches
states take in determining sales tax amounts.
Taxation differs not only in rates but also in the
rules of their application, which can significantly
affect the final prices of goods and services
depending on the state where the order is
processed and delivered. As a result, organizations
are forced to recalculate costs because goods may
be ordered in one state, where the tax approach
differs from another. For example, if a customer
places an order in one state but later changes the
delivery address to a state with a different tax rate,
the total order amount must be recalculated. In
most cases, this does not pose a problem, as
platforms can automatically update the invoice
and provide the customer with the correct amount.
However, the complexity arises when it comes to
loans [2].
The situation with taxes becomes more
complicated when a loan agreement is involved.
Upon signing a loan agreement, the payment
amounts are fixed and cannot be altered. However,
if the product is delivered to a different state with
a different sales tax rate, a legal and financial issue
arises. The tax amount should, in theory, change,
requiring a revision of the agreement terms, but
since they are already established, this is not
possible.
Such nuances in state-specific legislation become a
critical problem for fintech companies that offer
loans on goods or services, as well as for e-
commerce platforms operating across multiple
states. Companies are forced to adapt their tax and
financial strategies to account for potential sales
tax changes when delivery locations shift [3]. For a
clearer understanding of this issue, Figure 1 below
presents a map illustrating differences in sales tax
rates by state [4].
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Fig.1. Sales Tax by State 2024.
Providing a map showing varying tax rates is
crucial, as it highlights these differences and
emphasizes that companies need to rethink their
loan policies and account for this tax nuance to
minimize risks. The following recommendations
can help companies adapt to these conditions:
1. Implementing flexible contract terms.
Companies can include special clauses in loan
agreements that allow for recalculation in the
event of tax rate changes. This approach involves
creating flexible contracts that consider possible
changes in tax legislation when the customer’s
location shifts.
2. Developing integrated tax accounting systems.
Investing in automated tax management systems
that track regional sales tax rate changes will help
companies automatically recalculate amounts
before agreements are signed. These systems
should be integrated with product and service
pricing platforms to ensure that the correct tax
rate is applied based on the customer’s location
before contract signing.
3. Employee training and preparation. For the
successful implementation of new tax strategies, it
is important to train finance and legal department
staff. This will allow them to better navigate
regional tax differences and respond promptly to
tax rate changes. Companies should also
continuously upgrade employee skills to ensure
timely adaptation to new tax conditions.
4. Incorporating tax risks into loan assessment
models. Companies should revise their risk
assessment methods for loan offerings, including
tax risks. Developing detailed risk management
models that account for changes in tax burdens
depending on the region will help minimize
financial losses and adjust interest rates
accordingly.
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5. Using technological solutions for tax monitoring.
The use of modern technologies, such as
blockchain and artificial intelligence, to monitor
tax changes in real-time can become an important
tool for mitigating tax risks. These technologies can
automatically alert companies to tax rate changes
and provide opportunities for timely responses
[5].
Implementing these solutions will help minimize
the negative impact of tax rate differences, reduce
business risks, and improve conditions for
customers.
2. Operation Principle of the Tax Buffer
During the implementation of a project related to
online orders on the Amazon platform, it was
discovered that changing the customer’s address
or the fulfillment center after an order is placed
could result in tax adjustments, which might
increase or decrease depending on the
circumstances. This is because taxation depends
on where the order is actually processed. During
the development of payment methods, a credit
mechanism was proposed, which involves fixing
the credit amount at the time the order is created.
However, as the order is fulfilled, the credit may be
reduced but not increased due to existing legal
restrictions [7].
At a later stage of the project, it was revealed that
other payment methods, such as credit and debit
cards, have a different structure. They allow for an
increase in the payment amount after the order is
placed, followed by a downward adjustment. To
ensure the timely execution of the project, a
temporary solution was proposed. Initially, the
maximum possible tax rate for the state was set,
and then, during order processing, the tax was
recalculated, and the credit amount was reduced
using a special service call designed to adjust the
credit
limit.
This
service
call,
named
VoidLoanAmount, was developed to reduce the
credit amount in the case of partial order
cancellation.
During test checks conducted among employees
and their families, it was found that an excessive
number of credit cancellations could lead to legal
issues. Nearly 90% of the orders involved
adjustments to the credit amounts, raising
concerns since such a high figure could be
interpreted as suspicious activity by the fintech
partner, Affirm. This issue became a significant
obstacle to the further advancement of the project.
3. Technical Aspects of the Tax Operations
Buffer
Instead of presenting customers with inflated
contribution amounts followed by a reduction in
credit obligations, Amazon’s existing accounting
systems were utilized to automatically perform the
necessary calculations. This approach helped
avoid excessive notifications to customers about
intermediate
changes.
Amazon’s
internal
accounting requires that all financial operations
related to order fulfillment be fully aligned with
the inflow and outflow of funds. At each stage of
order fulfillment, data on revenue and taxes for
each individual shipment is recorded in the
accounting system. For example, if an order
consists of multiple shipments, the information for
each, including tax charges, is entered into the
system as each shipment is ready for dispatch.
Once all shipments are delivered, the total amount
is finalized in the accounting system, with the final
total matching the sum of all shipments. The
system also accounts for possible changes in tax
liabilities: the difference between the maximum
and minimum tax rates for the order is
automatically included in the calculation.
With each shipment, the API systems update the
accounting data to reflect any changes in the final
amount, as tax obligations may be adjusted at each
stage of delivery. If necessary, the system makes an
additional request to adjust the final amount in the
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cash register system. To ensure accuracy, both the
initial order price and the current cost of all
delivered shipments are tracked. After the
completion of all deliveries, these values are used
to calculate the tax buffer.
To implement this mechanism, a new table was
created in the AWS DynamoDB database.
Additional changes were made to the Java code,
allowing the system to update the table with each
new shipment. This solution enabled the project to
be completed on time, generating $1.7 billion in
revenue in its first year of operation. The payment
system was integrated into Amazon's checkout
pages, resulting in a sharp reduction of invalid loan
requests by 95%. This figure had a significant
impact on the company’s legal obligations to
Affirm.
Below, Table 1 describes the issues addressed by
the Tax Buffer mechanism.
Table 1. Problems solved by the Tax Buffer mechanism
Problem
Description
Solution through
the Tax Buffer mechanism
Need to adjust tax
obligations due to tax
changes
During product shipments, tax rates
may change depending on the
jurisdiction, requiring recalculation
of tax obligations for each
shipment.
The Tax Buffer mechanism
automatically recalculates tax
obligations when tax rates change,
reducing the risk of incorrect
calculations and accounting errors.
Reconciliation of cash
inflows and outflows
Ensuring an exact match between
cash inflows and outflows, which
becomes more complex with
multiple shipments and changes in
amounts.
The Tax Buffer ensures automatic
updates to the accounting ledger at
every stage of the order lifecycle,
guaranteeing reconciliation of
amounts across all shipment levels.
Customer notifications
about intermediate
changes in amounts
When tax amounts or costs change,
there is a need to notify customers,
which adds complexity and lowers
user satisfaction.
The mechanism eliminates the need to
notify customers of every change by
performing all calculations in the
background without customer
involvement.
Delays in accounting
for shipment changes
The need to update accounting data
after each shipment and tax amount
change. Failure to synchronize can
lead to accounting discrepancies.
The mechanism ensures automatic
data updates using APIs at each stage
of shipment, enabling prompt
adjustments to accounting records.
Risk of legal
consequences due to
incorrect accounting
Errors in tax calculations or credit
operations can result in legal
consequences and financial risks for
the company.
Automation through the Tax Buffer
reduces the number of errors in tax
and credit calculations, minimizing
legal and financial risks.
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High number of
invalid loan requests
Frequent errors in credit and tax
accounting lead to increased
inquiries and audits, putting
additional strain on systems and
legal departments.
The implementation of the Tax Buffer
reduced the number of invalid loan
requests by 95%, significantly easing
the load and lowering the risk of legal
disputes.
4. Practical Experience with the Tax Buffer
Method
As part of the Affirm project on the Amazon
platform, the goal was set to create a new payment
solution for customers in the U.S. and Canada. The
objective was to develop a system that would allow
customers to use credit to pay for products priced
over $50 on Amazon.com. In Canada, the credit
process also became available for local users
making purchases exceeding the same threshold.
Initially, Amazon had no such solutions in place,
which meant that implementing this system
required a complete overhaul of the interaction
mechanism with the fintech company Affirm.
Additionally, full integration with the existing
delivery and audit processes was necessary to
support the new payment method. Significant
changes were made to the payment processing
infrastructure, and new services were created to
handle every stage of the payment cycle.
The project was successfully implemented in 2021,
and its completion contributed to a substantial
increase in the company's revenue. In the first year
alone, the system generated significant profits.
Moreover, the launch of the new system in Canada
became part of Amazon's global strategy for
expanding payment solutions, helping the
company adapt to the global "Buy Now, Pay Later"
trend.
However, the implementation process was not
without challenges. One of the key issues was the
lack of a mechanism for processing credit
payments on the Amazon platform, which created
numerous uncertainties and required the
development of unique solutions. There were also
differences in tax regulations between the U.S. and
Canada, making it difficult to implement a unified
solution for both countries. To overcome these
obstacles, an innovative scheme was developed,
allowing the system to be adapted to the specific
features of both markets while meeting launch
deadlines.
The launch of the new payment method not only
enhanced the user experience but also laid the
foundation for further development of financial
services on the Amazon platform, strengthening its
market position. Below, Table 2 outlines the
advantages of this method.
Table 2. Advantages of the Tax Buffer method
Advantage
Description
Automation of tax
calculations
The Tax Buffer mechanism automatically recalculates tax obligations
when tax rates change, reducing human errors and increasing the
accuracy of calculations.
Optimization of accounting The process of maintaining records is simplified by automatically
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updating data at each stage of order fulfillment, improving the
accuracy of tracking inflows and outflows.
Reduction in operational
costs
Automating calculations and data synchronization reduces the amount
of manual work, decreases the need for human resources, and lowers
associated costs.
Elimination of the need to
notify customers of
intermediate changes
Customers no longer need to be notified of every change in tax rates
or order costs, making interactions with the system more transparent
and improving the user experience.
Minimization of legal risks
Reducing errors in tax calculations and credit operations lowers the
likelihood of legal disputes and claims from customers or regulators.
Increased efficiency of API
interactions
The Tax Buffer mechanism reduces the number of API calls related to
invalid loans, improving overall system performance and reducing
server load.
Consistent compliance with
tax regulations
The mechanism ensures that all tax obligations are accurately
calculated in accordance with changes in tax legislation, helping
avoid fines and penalties from tax authorities.
Acceleration of financial
operations
Fast data processing and synchronization at each shipment stage
significantly reduce the time required to complete financial
transactions, enhancing the company's overall efficiency.
Improved customer
experience
Simplifying and speeding up payment and tax calculation processes
enhances customer interactions, leading to higher satisfaction and
loyalty.
CONCLUSION
As a result of the analysis, the Tax Buffer
mechanism
was examined,
demonstrating
significant potential in addressing the issue of tax
changes in the fintech industry. Its successful
implementation not only automated tax
calculation processes but also reduced operational
costs, minimized legal risks, and improved overall
customer interaction efficiency. The future
potential of the Tax Buffer mechanism lies in its
adaptation to other regions and types of financial
operations, as well as in the possible improvement
of algorithms in response to constantly changing
tax regulations. This mechanism could serve as a
foundation for further innovations in the fintech
sector, promoting sustainable growth and
development in the long term.
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