Developing a Secure and Transparent Blockchain System for Fintech with Fintrust Framework

International Journal of Computer Networks & Communications (IJCNC) Vol.17, No.2, March 2025
DOI: 10.5121/ijcnc.2025.17208 125
DEVELOPING A SECURE AND TRANSPARENT
BLOCKCHAIN SYSTEM FOR FINTECH WITH
FINTRUST FRAMEWORK
Avinash Singh1
, Vikas Pareek 1
, Ashish Sharma 2
1
Department of Computer Science and Information Technology, Mahatma Gandhi
Central University, East Champaran, Bihar- 845401, India.
2
Department of Computer Science and Engineering, Manipal University,
Jaipur, Rajasthan 303007, India
ABSTRACT
The rapid growth of Fintech has driven the adoption of blockchain technology for secure, efficient, and
tamper-proof digital transactions. However, existing blockchain systems face challenges such as double-
spending attacks, inefficient consensus mechanisms, and limited trust management, which hinder their
scalability and security. To overcome these issues, this research proposes the Fin Trust Blockchain
Framework (FTBF), a multi-layered architecture designed to provide secure, scalable, and transparent
solutions for Fintech applications. FTBF integrates Zero Trust Architecture (ZTA) at its core to ensure
continuous user, node, and transaction validation. To prevent double-spending attacks, the Dynamic Coin
Flow Output Model (DCFOM) tracks unspent transaction outputs, ensuring the uniqueness of digital
tokens. The framework also introduces a novel consensus mechanism, the Time Elapsed Stake Secure
Algorithm (TESSA), which enhances scalability and energy efficiency. Additionally, the Fair Trust Rating
Server (FTRS) dynamically calculates and updates trust scores for network participants, storing them on a
trust score ledger for transparency and accountability. FTBF addresses key blockchainsecurity, efficiency,
and trust management limitations, paving the way for next-generation Fintech solutions with enhanced
scalability, resilience, and transparency.
KEYWORDS
Fintech, FinTrustBlockchain Framework, Zero trust Architecture, Consensus mechanism, Dynamic Coin
output Model, Digital tokens, Trust Score.
1. INTRODUCTION
The financial technology (FinTech) business has grown rapidly in recent years, driven by
technological developments to improve and streamline financial services [1]. One of the most
significant technologies to emerge in this field is blockchain, a decentralized, distributed ledger
system that allows for secure, transparent, and immutable record-keeping. Blockchain has
disrupted established financial institutions by introducing new ways to perform transactions,
secure data, and increase transparency [2]. Its use in FinTech has led to new opportunities for
peer-to-peer payments, digital currencies, smart contracts, and decentralized finance (DeFi),
among other innovations [3].At its core, blockchain stores data in a blockchain that is securely
linked to prevent tampering [4]. Each transaction or piece of information recorded on the
blockchain is validated by network participants using a consensus method to ensure its legitimacy
and integrity [5]. This decentralized model eliminates the need for intermediaries like banks and
payment processors, which lowers transaction costs, increases efficiency, and improves
security[6]. These properties make blockchain ideal for FinTech applications that value trust,

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speed, and cost-effectiveness.One of the most well-known blockchain applications in FinTech is
the development and maintenance of digital currencies like Bitcoin and Ethereum [7]. These
cryptocurrencies are based on blockchain technology, creating a secure, decentralized platform
for exchanging value without a central authority [8]. Using digital currencies in FinTech has
created new financial goods and services such as cryptocurrency exchanges, digital wallets, and
decentralized apps (dApps) [9]. The significance of blockchainin facilitating secure and
transparent transactions has also contributed to the emergence of DeFi, a movement aimed at
replacing traditional financial intermediaries with smart contracts and blockchain-based protocols
[10].Beyond cryptocurrencies, blockchain technology improves financial services such as
remittances, cross-border payments, and loans. Traditional remittance methods frequently have
high costs, poor processing times, and rely on intermediaries [11]. Blockchain-based solutions,
on the other hand, enable faster and more cost-effective cross-border payments by eliminating
middlemen and simplifying foreign exchange operations [12]. Similarly, blockchain is
revolutionizing the lending business by enabling peer-to-peer lending platforms, allowing
individuals to borrow and lend money directly without using traditional banks as intermediaries
[13]. Smart contracts are performed automatically when predetermined criteria are satisfied,
guaranteeing that all parties abide by the agreed-upon terms without manual intervention [14]. In
the financial sector, smart contracts are used for various objectives, including automating
insurance claims, streamlining trade financing, and improving clearing and settlement processes
[15]. By eliminating the need for middlemen and automating complicated operations, smart
contracts have the potential to drastically reduce costs and increase operational efficiency in the
financial industry.
Despite blockchain's multiple benefits to FinTech, its adoption is not without obstacles
[16].Scalability, legal ambiguity, and security concerns impede its incorporation into
conventional financial institutions. The energy consumption associated with certain consensus
processes, such as proof-of-work, is also an environmental concern [17]. However, ongoing
improvements in blockchain technology, such as creating more energy-efficient consensus
algorithms and forming clearer legal frameworks, are assisting in addressing these issues and
paving the way for wider implementation [18].Blockchain technology is changing the face of
financial transactions by providing a decentralized, secure, and transparent alternative to
established methods. Its FinTech applications are wide, including cryptocurrencies, cross-border
payments, smart contracts, and decentralized financing [19]. While obstacles persist, blockchain
has enormous potential to change the financial industry, and its continuing evolution promises to
drive even more innovation in the field. As blockchain technology improves and gains traction, it
is expected to play an increasingly important role in the future of financial transactions, creating
new opportunities for businesses, consumers, and investors alike.However, systems in FinTech
face several limitations that hinder their scalability, security, and overall performance. One major
issue isscalability, as many current blockchain networks struggle with handling high transaction
volumes due to inefficient consensus mechanisms and limited throughput. This results in slow
transaction processing times and increased costs, especially during periods of high demand.
Additionally,double-spending attacksremain a critical concern, with many systems being
vulnerable to fraudulent activities where the same digital token can be spent multiple times,
undermining trust and security.Trust managementis another limitation, as existing systems often
rely on static trust models that fail to dynamically assess and update the trustworthiness of
network participants, leaving room for malicious actors to exploit vulnerabilities. Moreover,
certain consensus algorithms, such as proof-of-work, consume significant energy, raising
environmental concerns by addressing these issues and introducing a novel approach to
continuously validate users and nodes to enhance energy efficiency and scalability while ensuring
high transaction throughput. These improvements aim to make blockchain systems more secure,
efficient, and scalable for FinTech applications.The key contribution of this proposed work is as

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follows: to provide a comprehensive solution to the existing challenges in Blockchain systems for
FinTech promoting secure, efficient and scalable digital transactions,
 A novel framework FTBF designed to enhance security, scalability and transparency in
FinTech by integrating advanced blockchain.
 A dynamic trust management system based on ZTA that ensures continuous validation of
users, nodes, and transactions, removing assumptions of inherent trust and strengthening
the overall security posture.
 A new approach, DCFOM, is introduced to preventdouble-spending attacks by efficiently
tracking unspent transaction outputs, ensuring the integrity and uniqueness of digital
tokens in the system.
 A TESSA algorithm improves energy efficiency and scalability while maintaining high
transaction throughput in blockchain networks.
 A dynamic system that computes and updates trust scores for all participants based on
their behaviours and actions, ensuring that access and transaction approval are contingent
upon the trustworthiness of entities.
The structure of this research document is organized into several key sections; following the
introduction, section 2 provides the literature review, which discusses existing studies, identifies
the research gap and establishes the need for the proposed framework. Section 3 presents the
definition of the problem and outlines the key issues. Section 4 details the proposed methodology
and explains the architectural design and components of FTBF. Section 5 outlines the
implementation and experimental setup and provides the results. Finally, Section 6 concludes
with a conclusion and a discussion of future work.
2. LITERATURE REVIEW
The application of blockchain technology in the financial sector has gained significant traction
due to its potential to enhance security, transparency and efficiency. Several researchers have
explored diverse methodologies and frameworks to address various challenges in Fintech,
banking and data security.
Rjoub et al. [20] exploredusingblockchain-based financial technology (FinTech) in the banking
industry to overcome transition difficulties. Their research looked at important FinTech aspects
that influenced the success of Chinese banks. They suggested an adaptive neuro-fuzzy-based K-
nearest neighbour’s method optimized with a chaotic enhanced foraging algorithm to improve
predictive accuracy. However, the study has some drawbacks, including a reliance on specific
FinTech elements that may not be applicable to other banking scenarios. Furthermore, the chaotic
nature of the optimization process caused possible convergence concerns, and the rolling window
model encountered challenges in capturing abrupt, significant scale shifts in FinTech growth.Gai
et al. [21] proposed a blockchain-based access control mechanism using a consortium blockchain,
Role-Based Access Control, multi-signature protocols, and smart contracts to enable secure and
efficient data exchange. Tested on the HyperLedger Fabric platform, the solution proved
effective but faced challenges, including scalability issues for large networks, limited
compatibility with public blockchains, and performance concerns due to the computational
complexity of multi-signature protocols.
Chaudry et al. [22] developed a blockchain-based algorithm for online transactions that protects
bank resources from malicious users and secures transactions using a zero-trust security method.
The framework and algorithm were created based on previous research and literature reviews.
The idea was to improve financial security by implementing blockchain technology and zero-

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trust principles. However, scalability issues may occur as the framework evolves to handle higher
transaction volumes, and reliance on blockchain consensus methods may increase latency during
peak periods.Liu et al. [23] proposed a blockchain-enabled information-sharing solution for zero-
trust scenarios, ensuring anonymity, data privacy, trustworthiness, and equitable participant
stimulation. The approach used smart contracts for filtering falsified information and effective
voting and consensus methods to prevent misinformation. Security was verified using the
universal composability framework, and performance was evaluated on an Ethereum-based
platform. However, reliance on Ethereum raises concerns about transaction costs and network
congestion in large-scale scenarios.
Song et al. [24] introduced blockchain technology to design a four-layer architecture and multiple
trust evaluation indicators based on blockchain service data. They proposed a blockchain-based
FinTech trust evaluation mechanism (BFTEM), which records relevant data and multiple trust
parameters during block transmission. The mechanism verifies the trust degree issued by the trust
holder through a comprehensive trust value of the user. Simulation experiments showed that the
BFTEM mechanism improved the security and reliability of FinTech data, enhancing trust
evaluation accuracy and expanding its potential applications. However, the mechanism's
vulnerability to double spending attacks and reliance on blockchain data makes it vulnerable to
inaccuracies or manipulation during transmission.Wang et al. [25] introduced a Software Defined
Perimeter solution within Zero Trust Architecture (ZTA) to enhance security for financial trading
terminals. By relocating security protection to the user access layer, the solution eliminates
VPNs, boosts remote access security, and improves transaction security. Key achievements
include deep integration with transaction systems, minimal impact on terminal performance,
tailored security setups, and advanced technologies like single-packet authorization and
secondary authentication. However, further exploration is needed to address scalability and
adaptability to rapidly emerging cyber threats.
Song et al. [26] created a Multi-Dimensional Trust Index System and Evaluation Mechanism
(MDTEM) for FinTech that considers direct, indirect, recommendation, and feedback trust. To
improve financial services' security, reliability, and trustworthiness, a four-level blockchain
structure was developed, includingblockchain at the cloud, internet, contract, and application
levels. Trust simulation research revealed that the MDTEM considerably improved the security
and dependability of FinTech trust applications. However, limited trust management capabilities
made conducting consistent trust evaluations in large-scale, dynamic FinTech systems
difficult.Bahar et al. [27] developed a Metric-Based Feedback Methodology (MBFM) for
improving corporate security by combining bug bounty programs with threat modelling. The
methodology aids in identifying root causes and refining threat models, hence improving the
effectiveness of security measures. The study defines the methodology's assumptions and is a
foundation for future research. However, it necessitates continual updates to threat models, which
presents scalability issues for large and dynamic systems.
This reviewed existing literature illustrates the diverse applications of blockchain technology and
Zero Trust principles in FinTech, banking, and access control systems. While significant progress
has been made in enhancing security, scalability, and efficiency, several critical gaps remain.
These include challenges related to scalability in large networks, where existing frameworks
often struggle to handle high transaction volumes and achieve optimal throughput. Furthermore,
system latency continues to be an issue, particularly in blockchain solutions relying on heavy
consensus protocols like proof-of-work, which can increase delays during peak transaction
periods. Convergence issues in optimization, especially in chaotic or complex algorithms, lead to
suboptimal solutions and decreased performance. Additionally, existing systems remain
vulnerable to double-spending attacks, where digital tokens may be fraudulently used more than
once, undermining the integrity of transactions. Limited trust management in many blockchain-

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based solutions remains a concern, as many rely on static or simplified trust models that fail to
assess and update participant trustworthiness dynamically. The proposed framework addresses
these gaps to improve scalability and energy efficiency while enhancing system performance.
These advancements aim to fill the gaps identified in the literature, providing a more secure,
scalable, and efficient solution for large-scale financial systems.
3. PROBLEM DEFINITION
The growing adoption of blockchain technology in FinTechhas revealed several significant issues
affecting blockchain-based financial systems' efficiency, security, and scalability. Despite the
promise of decentralization and immutability, blockchain platforms have intrinsic constraints
preventing widespread use. This section discusses the key concerns the FinTrustBlockchain
Framework (FTBF) seeks to address, such as double-spending attacks, inefficient consensus
techniques, and limited trust management. Bahar et al. [27] developed a Metric-Based Feedback
Methodology (MBFM) for improving corporate security by combining bug bounty programs with
threat modelling. The methodology aids in identifying root causes and refining threat models,
hence improving the effectiveness of security measures. The study defines the methodology's
assumptions and is a foundation for future research. However, ongoing upgrades to threat models
posing scalability challenges for large and dynamic systems are required. One of the most serious
security concerns in blockchain systems is the possibility of double-spending attacks, in which a
single digital token is spent more than once. This occurs when malicious users leverage network
slowness or computational delays in transaction validation to create duplicate tokens. Traditional
blockchain frameworks use Proof-of-Work (PoW) or Proof-of-Stake (PoS) procedures to protect
against such attacks. However, these approaches are computationally demanding and vulnerable
to fork-based attacks that jeopardize system integrity. Blockchain networks rely on consensus
methods to ensure agreement on the authenticity of transactions. Traditional systems, such as
PoW, need a lot of energy, but PoS has concerns with "rich-get-richer" dynamics. These
inefficiencies lead to slower transaction processing, significant computing overhead, and limited
scalability.Financial transactions rely heavily on trust, particularly in decentralized blockchain
contexts where participant anonymity is frequent. Existing blockchain systems frequently rely on
implicit trust, which hostile nodes can exploit to launch Sybil attacks or initiate fraudulent
transactions. The absence of ongoing trust evaluation and verification undermines openness and
system accountability. Thus, the proposed framework addresses these issues by introducing
innovative techniques to enable a secure, efficient, scalableblockchain framework for FinTech
applications.
4. PROPOSED METHODOLOGY
The proposed FinTrustBlockchain Framework (FTBF) is a robust, secure, and scalable
blockchain solution that aims to address the shortcomings of current blockchain-based FinTech
systems. Traditional blockchain systems face a number of significant difficulties, including
vulnerability to double-spending attacks, inefficient consensus techniques that result in high
computing costs, and inadequate trust management, which diminishes system transparency and
security. The FTBF proposes a multi-layered architecture with advanced components and unique
methodologies to address these difficulties. The DCFOM reduces double-spending by tracking
Unspent Transaction Outputs in real-time, ensuring that each digital token is only spent once.
TESSA improves consensus efficiency by combining PoS with PoET, lowering computing
overhead and energy usage. The framework uses a ZTA to manage trust, requiring every user,
node, and device to undergo continuous verification before access is permitted. Additionally, the
FTRS dynamically reviews and updates trust scores for users and nodes, which are then recorded
in an immutable Trust Score Ledger on the blockchain. This comprehensive trust structure

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ensures responsibility and deters fraudulent activities. The architectural design of FTBF in Figure
1 is based on a multi-layered blockchain architecture to ensure modularity, scalability and
efficient transaction processing.
Registration
User Interaction &
Transaction request,
Wallet, User
Authentication, Encryption
Transaction
layer
Double Spending attack
mitigation
Consensus Mechanism
optimization
Validation
Layer
Trust Management
Enhancement for
continuous verification
Trust update system
Trust
Management
Layer
Fraud Detection and
Blacklisting
Block Finalization
Figure 1: Architecture of Proposed Work
The architectural design of FTBF in Figure 1 is based on a multi-layered blockchain architecture
to ensure modularity, scalability, and efficient transaction processing. In Figure 1, the system
architecture is divided into three primary layers: (1) Transaction Layer: Handles user interactions,
transaction creation, and encryption via RSA before sending the data to the validation layer. (2)
Validation Layer: Ensures transaction validity, prevents double spending through the DCFOM,
and employs the TESSA algorithm for energy-efficient consensus. (3) Trust Management Layer:
Enforces ZTA for continuous identity verification and uses the FTRS to maintain and update trust
scores recorded in the blockchain-based Trust Score Ledger. These layers work together to
provide a secure, efficient, and scalable infrastructure for FinTech applications.
4.1. Transaction Layer
This layer is the entry point of user interaction with the blockchain. It facilitates transaction
creation, user authentication and secure submission of transaction requests. This layer manages
user interactions, transaction initiation, data encryption and secure transfer of transaction details
to the subsequent validation layer. This layer establishes a safe and user-friendly environment for
transaction processing by incorporating robust encryption mechanisms and user authentication
protocols. Figure 2 shows the processing flow of user interaction with the blockchain.

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Start
Enter Transaction Page
Login or not User registration
Enter information
(Account, name,
password, email)
Smart contract
verification account is
duplicated or not
Is the registration
successful?
No
Enter transaction details
(recipient account, amount,
metadata)
Smart Contract
validation
Reject
Is the transaction is
valid
No Yes Proceed with encryption
and submit to
blockchain network
End
Yes
No
Yes
Figure 2: Initial Process of entity of blockchain
4.1.1. User Wallet and Asset Management
User Wallets are the primary interfaces for users to store, manage, and access their blockchain
assets. Each user receives a secure digital wallet that stores private keys, public keys, and
blockchain addresses. These wallets include multi-factor authentication (MFA) and biometric
verification features to provide an extra layer of security. Users can access their wallets to
monitor their transaction history, check their asset balances, and begin new transactions. Each
wallet is uniquely identified by a cryptographic public-private key pair (𝐾𝑝𝑢𝑏, 𝐾𝑝𝑟𝑖𝑣). The 𝐾𝑝𝑢𝑏
is openly shared and acts as an address for receiving funds, while 𝐾𝑝𝑢𝑏 is confidential and used to
sign transactions.Let the balance 𝐵 of a user’s wallet be represented as
𝐵𝑢𝑠𝑒𝑟 = ∑ 𝑈𝑇𝑋𝑂𝑖
𝑛
𝑖=1 (1)
Thus, user wallets play a crucial role in FTBF by storing and managing Unspent Transaction
outputs (UTXOs), which represent the user's available balance. They generate and maintain the
user’s public-private key to enable secure encryption, decryption, and authentication of
transactions. Additionally, user wallets track and display the 𝐵𝑢𝑠𝑒𝑟, providing real-time updates
on available funds for seamless transactions.
4.1.2. User Authentication and Identity Verification
Before a user can initiate a transaction, the FTBF enforces a severe authentication process to
ensure that only confirmed and legitimate users utilize the system. Authentication verifies the
user's identity using cryptographic methods such as digital signatures and multi-factor
authentication. This step is critical for preventing unwanted access and ensuring all transaction
requests come from a trustworthy source. The user signs the transaction request 𝑇using the𝐾𝑝𝑟𝑖𝑣,
the signature 𝜎 is generated using a signing algorithm as,

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𝜎 = 𝑆𝑖𝑔𝑛(𝑇, 𝐾𝑝𝑟𝑖𝑣) (2)
Then, the signature 𝜎 is validated using the corresponding 𝐾𝑝𝑢𝑏 to ensure the authenticity and
integrity of the transaction request.
𝑉𝑒𝑟𝑖𝑓𝑦(𝑇, 𝜎, 𝐾𝑝𝑢𝑏) = 𝑇𝑟𝑢𝑒 (3)
If the verification is true, the system confirms that the transaction originated from a legitimate
user.
4.1.3. Transaction Request Creation
Once a user has been authenticated, they can create a transaction request.This request provides
essential transaction information, such as the recipient's blockchain address (sender 𝐾𝑝𝑢𝑏 (acting
as wallet address), receivers𝐾𝑝𝑢𝑏), the amount to be transferred, and any necessary metadata for
processing. The user is presented with a transaction preview for review and confirmation. This
phase allows the user to verify the accuracy of the transaction data before submission.
4.1.4. Transaction Encryption
To ensure the confidentiality and integrity of transaction data, the FTBF uses RSA (Rivest-
Shamir-Adleman) encryption. RSA is a well-known cryptographic technique for its strength and
capacity to protect sensitive data. After the transaction request is confirmed, the system encrypts
all transaction details, including the recipient's address, amount, and metadata, with the
recipient's𝐾𝑝𝑢𝑏. This encryption procedure ensures that only the intended recipient may decrypt
the transaction details using𝐾𝑝𝑟𝑖𝑣, safeguarding user privacy and preventing data interception.
The plain text transaction 𝑇 is converted into an integer 𝑚 using a padding scheme, the cipher
text 𝑐 is generated using RSA encryption 𝑐 = 𝑚𝑒
𝑚𝑜𝑑𝑛 where 𝑒 is the public exponent of RSA
key pair 𝑛 is the modulus which is product of two large prime numbers. This cypher text 𝑐
represents the encrypted form of transaction. The encryption ensures that the transaction cannot
be intercepted or read during transmission.
4.1.5. Transaction Submission to the Validation Layer
After encryption, the transaction request is securely sent to the Validation Layer. The encrypted
data is transmitted across a secure channel along with the sender’s signature𝜎 for validation,
protecting it from external risks such as eavesdropping, tampering, or interception. This
transmission mechanism is enhanced by using Secure Sockets Layer (SSL) or Transport Layer
Security (TLS) protocols to safeguard data in transit. The Transaction Layer also assigns each
request a unique transaction identifier (TXID), allowing users to trace their transaction status in
real-time. During the submission, the integrity of the transaction is verified using hashing. The
system computes a hash 𝐻(𝑇) of transaction requests using a cryptographic hash function using
RSA. The𝐻(𝑇)is stored along the transaction to ensure data integrity. If any data is modified, the
computed hash will not match the stored hash, raising an alert.
4.2. Validation layer
This layer is the core processing unit of FTBF, which aims to ensure the validity of transactions,
prevent double spending and achieve efficient consensus. This layer plays a vital role in verifying
and validating transactions before they are permanently added to the blockchain. Through a

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combination of robust functions of DCFOM and TESSA, the validation layer guarantees the
system's security, integrity and efficiency.
4.2.1. Dynamic Coin Flow Output Model
DCFOM is a novel mechanism designed to track UTXOs and prevent double spending, and it
monitors and maintains a time record of UTXOs within the blockchain. A DCFOM is the amount
of digital cash transmitted to a crypto address or remaining after a transaction. Transactions
generate new UTXOs, which can be used in subsequent transactions. Each UTXO is treated as a
token, with no closing balance[28]. Transferring a UTXO from one party to another involves
transferring ownership, rather than reconciling two databases. UTXO structures offer advantages
over standard account models, such as increased security and the capacity to simultaneously
process many transactions from a single-payer.The DCFOM reduces double-spending by tracking
Unspent Transaction Outputs in real-time, ensuring that each digital token is only spent once.To
facilitate reproducibility, the DCFOM implementation involves a ledger system where every
transaction is cross-checked with a real-time database of UTXOs. The framework ensures that
any token reuse attempt is flagged by comparing the transaction inputs against the existing set of
unspent outputs. This can be implemented using a hash-based mapping of transaction outputs,
enabling rapid verification of the token’s state, thus preventing double spending in a transparent
and traceable manner.
Consider three users: Alice, Bob, and Charlie. Their private keys, public keys, and wallet
addresses are {𝛿𝐴, 𝛽𝐴, 𝜇𝐴}, {𝛿𝐵, 𝛽𝐵, 𝜇𝐵}, and {𝛿𝐶, 𝛽𝐶, 𝜇𝐶}, Alice has a UTXO of 15 coins. She
sends 10 coins to Bob (𝜇𝐵) and receives 5 coins as a change to her sending address (𝜇𝐴). During
transaction 1𝑇𝑥1, Alice must prove ownership of the input address. To verify ownership, she
supplies her public key (𝛽𝐴) and a signature generated by signing 𝑇𝑥1 with her private key (𝛿𝐴).
Verifying the signature using (𝛽𝐴) confirms that the owner has the corresponding private key
for(𝜇𝐴). Similarly, Bob transfers 6 coins to Charlie 𝜇𝐶 from Alice's UTXO and receives a
reimbursement of 4 coins. Both transactions are maintained on a blockchain accessible to all
network participants. During the initial transaction, Alice recognizes that 𝜇𝐵 belongs to Bob.
However, this transparency creates a potential privacy risk as Alice or others can track all
transactions associated with𝜇𝐵, revealing Bob’s balance [29].To mitigate such privacy risks,
DCFOM changes the wallet address after each transaction, making it more difficult to trace
transaction history or link balances to specific users. When initiating a new transaction, the
DCFOM verifies whether the related outputs have already been spent. If duplicate outputs are
detected, the transaction is flagged for further examination. Furthermore, the Trust Management
Layer may face penalties or increased scrutiny on the user or node responsible for the duplicate
outputs. This system assures no token or coin is used more than once, prohibiting fraudulent
conduct like double-spending. This solution improves blockchain transaction security and
anonymity by combining robust tracking and verification processes with advanced privacy
measures.
4.2.2. Time Elapsed Stake Secure Algorithm
TESSA is a hybrid consensus mechanism that combines the strengths of PoS and Proof of
Elapsed Time (PoET) to achieve fast, energy-efficient and secure consensus. The PoS component
selects validators based on the proportion of participants' tokens or stakes. This method ensures
that users with a higher stake are more likely to be chosen as validators. By rewarding
stakeholders, PoS inherently encourages active participation and honest behaviour within the
network, reducing the risk of Sybil attacks and improving overall network security[30].PoS
mechanisms offer faster transaction confirmation than PoW mechanisms, in addition to their low
energy consumption. In a blockchain network, transaction confirmation is based on transaction

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throughput and block confirmation time. Transaction throughput (𝑇𝑋𝑠) is important for network
performance, especially when there are many pending transactions[31]. 𝑇𝑋𝑠can be calculated as
𝑇𝑋𝑠 =
𝐵𝑙𝑜𝑐𝑘𝑠𝑖𝑧𝑒
𝑇𝑥𝑠𝑖𝑧𝑒×𝐵𝑙𝑜𝑐𝑘𝑡𝑖𝑚𝑒
(4)
On the other hand, the PoET component introduces unpredictability and randomness into the
selection process. Based on Intel’s Software Guard Extensions (SGX), Intel proposes Sawtooth
Lake, which uses "proof-of-elapsed-time" (PoET) to regulate the building of new blocks [32]. In
PoET, each participant is given a random waiting period, and the person whose timer runs out
first is chosen as the validator for the following block. This unpredictability prevents any person
from controlling the validation process, maintaining fairness and impartiality[33]. In contrast to
PoW, PoET does not require energy-intensive mining, making it a more sustainable approach to
consensus. TESSA improves consensus efficiency by combining Proof of Stake (PoS) with Proof
of Elapsed Time (PoET), lowering computing overhead and energy usage. The implementation of
TESSA involves setting up a hybrid consensus model where nodes participate in validating
transactions by staking tokens for PoS while using TEEs to generate random timers for leader
election in PoET. The PoS mechanism determines validators based on token holdings. PoET
ensures that leader selection is secure and energy-efficient by utilizing trusted execution
environments that generate unpredictable wait times, reducing the overall computational burden.
When a new block is validated, TESSA uses a combination of PoS and PoETto select a validator,
enabling the network to strike a balance between efficiency and justice. The chosen validator
verifies the transactions within the block to ensure they meet all required conditions, such as the
absence of double spending, sufficient account balances and proper cryptographic signatures.
Once the transactions are verified, the validator finalizes the block and adds it to the blockchain.
This process is faster and more energy efficient than traditional PoW mechanisms. For
reproducibility, the setup involves defining a protocol for node selection based on pre-set criteria
for PoS and configuring the TEE environment for random time generation. Detailed parameters
for both mechanisms, such as the staking amount for PoS and the minimum execution
environment requirements for PoET, can be standardized to ensure the system's consistency
across implementations.
4.3. Trust Management Layer
This layer is critical to preserving the blockchain network's integrity, security, and dependability
by constantly monitoring and assessing the behaviour of nodes, users, and transactions. This layer
is built on ZTA, a security concept in which no internal or external entity is trusted by default.
Instead of implicitly trusting nodes after initial verification, ZTA mandates continual verification
and re-evaluation of all participant activities and transactions to ensure compliance with set
security guidelines. This technique reduces the danger of unauthorized access or potential
security breaches by implementing a dynamic and watchful trust management system. Fig. 3
illustrates a model that integrates zero trust principles and blockchain technology. Blockchain
technology enables decentralization and immutability of data, while zero trust principles are used
for access control and authorization where no user, device, or node is trusted by default, and trust
is continuously verified and recalculated.To develop a trust management system for blockchain-
enabled environments using FTRS, a critical component in computing and updating the trust
scores of users and nodes.

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Sender
Send
Request
TSL
Send file
Send hash
Retrieve hash
Request
for
file
Receive File
Blockchain
Figure 3: Integration of Zero Trust with Blockchain for Fintech
The FTRS calculates and manages these trust scores, offering a transparent and tamper-proof
mechanism for monitoring network participants' trustworthiness. Then, it continuously analyses
activity and flags questionable behaviour, modifying nodes' trust levels accordingly. The Trust
Score Ledger (TSL), which is kept on the blockchain, provides an immutable record of trust
scores and acts as the foundation for executing access control regulations based on trust level.
Table 1 lists several components along with their descriptions for calculating the trust score.
Table 1: Components and Description used for Trust score calculation
Component Description
FTRS
The centralized or distributed unit responsible for calculating trust scores
using zero-trust principles
User/Nodes
Participants in the system that request access to resources or interact with
blockchain
Data Collector
Gathers data from user activities, access logs, node behaviour and
transaction history
TSL
A secure blockchain-based ledger where trust scores and access logs are
recorded for transparency and traceability.
Access control gateway Validates trust scores before granting access to blockchain resources.
The FTRS in a ZTA ensures dynamic and safe access control by constantly reviewing trust
ratings for users and nodes. The process begins with initialization, which determines critical
characteristics such as user behaviour, access history, and anomaly status. The features
(𝐹1, 𝐹2, … 𝐹𝑛) are normalized to guarantee consistency in calculations. Each attribute is allocated
a weight (𝑤1, 𝑤2, … 𝑤𝑛)based on its relevance, ensuring the aggregate of weights equals 1
(∑ 𝑤 = 1). Trust score thresholds () are also specified to help with access decisions.Once
initialized, the trust score 𝑇𝑖 for each user or node 𝑖 is computed using the formula,
𝑇𝑖 = 𝑤1. 𝐹1 + 𝑤2. 𝐹2 + 𝑤3. 𝐹3 + ⋯ . 𝑤𝑛. 𝐹𝑛 (5)
This technique dynamically combines weighted feature values to generate an overall trust score.
For example, a node with regular behavioural patterns and a good access history will obtain a
better trust score. In contrast, anomalies or deviations from predicted behaviour reduce the
score.The trust update method ensures scores remain relevant by tracking and incorporating real-
time changes. User actions such as successful logins raise the score, while failed attempts lower
it. Behavioural monitoring detects departures from usual patterns and updates the score
accordingly. Updates to device health, such as installing security patches, impact the trust score.
Any detected anomalies, such as unauthorized access attempts or irregular activities, will result in

136
sanctions. Each update is stored on a blockchain-based TSL, guaranteeing immutability and
transparency. The ledger records the timestamp, updated trust score, the reason for the change,
and magnitude of adjustment (𝛥𝑇).Access decisions are made based on the updated trust score. If
𝑇𝑖 ≥ 𝜏𝑔𝑟𝑎𝑛𝑡, the user or node gains access. If 𝑇𝑖 < 𝜏𝑔𝑟𝑎𝑛𝑡, access is refused and the node is
marked for review. If a node's score falls below 𝜏𝑑𝑒𝑛𝑦it is considered potentially fraudulent and
may be blacklisted or investigated further. Persistently low trust scores across many time frames
result in block finalization, which prevents malevolent nodes from accessing the system.
For Grant access, 𝐴𝑐𝑐𝑒𝑠𝑠 𝐷𝑒𝑐𝑖𝑠𝑖𝑜𝑛 = {
𝐺𝑟𝑎𝑛𝑡, 𝑖𝑓 𝑇𝑖 ≥ 𝜏𝑔𝑟𝑎𝑛𝑡
𝐷𝑒𝑛𝑦, 𝑇𝑖 < 𝜏𝑔𝑟𝑎𝑛𝑡
(6)
If 𝑇𝑖 falls below 𝜏𝑑𝑒𝑛𝑦, the node is flagged as a potential insider threat or malicious actor.
This structured and dynamic approach has numerous key advantages; dynamic trust updates
ensure that trust scores are updated depending on user behaviour and transactional patterns,
allowing for flexible decision-making. The system prohibits implicit trust by adhering to
ZTA principles, effectively decreasing insider risks. The FTRS's scalability enables it to manage
thousands of users and devices simultaneously, providing seamless operation even in large-scale
networks. Enhanced access control is achieved by restricting access to low-trust users or nodes,
reducing the danger of illegal activity. Furthermore, a blockchain-based TSL ensures
transparency and auditability by keeping a secure and immutable record of trust score revisions.
Together, these traits enable the FTRS to mitigate insider threats, enhance access control and
maintain robust security, which allows ZTA to function effectively in dynamic and complex
environments.
5. RESULTS AND DISCUSSION
In this research, Python is utilized in the simulation experiment to analyse blockchain
performance in FinTech, as it is a popular simulator for testing mobile networks and
payments. Experimental analysis uses data from banking institutions and FinTech companies and
computer simulations. The simulation experiment setup includes 10 servers and 200 FinTech
users. FinTech users are spread across a 10𝑘𝑚 × 10𝑘𝑚 radius for data processing and
transmission. The user trust rates are 70%, 80%, 𝑎𝑛𝑑 90%, respectively. Simulation experiments
can run on an average of 500 𝑠𝑒𝑐𝑜𝑛𝑑𝑠, resulting in 200 𝐹𝑖𝑛𝑇𝑒𝑐ℎtransfers every
second. Execute numerous simulations with different parameter values for each scenario, then
average the results. FinTech users' payment arrival times reflect the Poisson distribution based on
normal random variables. The essential parameters are listed in Table 2.
Table 2: Listing of Essential Parameters
Parameter Value/Range
Number of Fintech users 200
Number of Servers 10
Transmission radius of Fintech user 500m
Encryption method RSA (2048 bit)
Transaction size 250B, 500B, 1KB
Number of Fintech transactions per second 100/s
User Authentication Public Key Infrastructure
Transaction timed out 60s
Simulation time 500s
Trusted service values 80%
Maximum number of Attacks 1000

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In the FTBF trust evaluation simulation experiment, data transmission increases from 1 ×
103
to 10 × 103
. The verification time for FinTech data transfer is defined by the time it takes to
calculate the suggested value based on blockchain trust value. Validation and request processing
times scale linearly with transaction and query volume. The trust assessment mechanism allows
numerous trust evaluations to be applied to the same FinTech data processing. Each trust
evaluation may be specified based on the trust needs associated with that process.
5.1. Performance Parameters
 Block Generation: The time required to create and add a new block to the blockchainis
influenced by block size, transaction processing speed and consensus mechanism
efficiency.
 Trust Rate: The percentage of honest or trustworthy nodes actively participating in
blockchain impacting system reliability, node selection and resistance to Sybil attacks.
 Delay: The total time taken from the transaction initiation to its final confirmation in
blockchain, including network propagation, validation and block finalization.
 Throughput: The number of transactions successfully processed and confirmed per
second, reflecting the network's ability to handle large transaction volumes efficiently.
 Comprehensive Trust Value:This holistic measure of a node’s trustworthiness is
calculated using historical behaviour, successful validations, and ensuring secure and fair
participation.
5.2. Simulation Results Analysis
The simulation results of FTBF are analyzed to assess its performance in terms of block
generation, trust rate, throughput and delay. The proposed system is evaluated under varying
network conditions, node participation and transaction loads. The analysis shows that FTBF
significantly decreases block generation time due to its efficient hybrid consensus mechanism
and simplified transaction validation process. The trust rate is constantly high, thanks to the
ZTA and FTRS, which ensure that only trustworthy nodes participate in consensus. Throughput
increases, suggesting greater transaction processing capability, while time is reduced due to
effective processing at the Validation Layer. The comprehensive trust value emphasizes the
framework's capacity to keep the network intact and fair across nodes.
Figure 4 demonstrates the relationship between block generation time and the number of FinTech
users across varying trust levels (70%, 80%, and 90%). The analysis reveals that the trust levels
influence the efficiency of block production in the FTBF. As trust values increase, block
generation time decreases significantly. This is attributed to the faster validation and consensus
process facilitated by the hybrid TESSA mechanism and enhanced trust management protocols.
The graph shows a clear trend: higher trust levels reduce block generation time, particularly as
the number of users increases. As trust levels rise, block creation time drops by up to 90%,
highlighting the FTBF's capacity to maintain high efficiency even in large-scale FinTech
environments. This improvement is further supported by the integration of DCFOM and TESSA,
which improves scalability and reduces computational overhead, making the system more
adaptable to the increasing size of networks. The results indicate that the FTBF, with its
innovative trust-based mechanisms, is well-suited for handling the demands of large FinTech
applications without compromising performance.

138
Figure 4: Block generation time analysis
Figure 5 illustrates the variation in trust rates within FinTech systems at initial trust levels of
70%, 80%, and 90% as the number of attacks increases from 0 to 1000. The adaptive behaviour
of the Trust Management Layer is evident from the results, as the trust rate for each initial trust
value increases despite the rise in attack frequency. This adaptation is attributed to the system's
ability to adjust trust computations and dynamically reinforce resilience against malicious
behaviour. The graph highlights that systems with higher initial trust levels, such as 90%,
demonstrate remarkable stability under attack scenarios, maintaining a trust rate exceeding 95%
even after 1000 attack incidents. This implies that the FTBF's mechanisms, including its Trust
Management Layer and hybrid TESSA framework, effectively mitigate the impact of repeated
attacks by quickly adapting and recalibrating trust evaluations. Furthermore, the results indicate a
significant advantage for systems with higher baseline trust values, as they exhibit superior
performance and reliability under stress. This stability enhances user confidence in the system's
robustness, even in sustained cyberattacks. The findings emphasize the FTBF's capability to
ensure trust and reliability in large-scale FinTech networks, making it a resilient solution for
secure and efficient operations in dynamic and adversarial environments.
Figure 5: Trust rates under three trust values
Figure 6 illustrates the variation in blockchain computation time as transactions increase under
varying conditions. The results reveal a proportional relationship between the number of
transactions and the computation time, highlighting the system's scalability challenges under
higher transaction loads. The computation time begins at approximately 10 seconds for 5
transactions, increases to around 20 seconds for 10 transactions, reaches 25 seconds for 15
0
5
10
15
20
25
30
0 40 80 120 160 200
Block
Generation
time
(μs)
Number of Fintech users
70%
80%
90%
70
75
80
85
90
95
100
0 200 400 600 800 1000
Trust
rate
%
Number of attacks on FinTech
70%
80%
90%

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transactions, and rises to 40 seconds for 20 transactions.This increasing trend demonstrates that
while the FTBF framework can handle a growing number of transactions, the computation time
scales upward linearly, reflecting the computational complexity associated with validating and
recording transactions on the blockchain. This trend underscores the importance of introducing
optimization mechanisms, such as parallel processing or dynamic load-balancing techniques, to
enhance the efficiency of blockchain operations under high transaction volumes.The findings
highlight the need for further refinement in the FTBF system to address potential bottlenecks and
maintain performance consistency in real-world applications with substantial transaction
demands. These results emphasize the system's scalability while pointing out opportunities for
improving processing speeds to support larger-scale FinTech deployments effectively.
Figure 6: Computation time vs. number of transactions
5.3. Comparative Analysis
Figure 7 compares the throughput performance of four trust algorithms such asEFMCDM [37],
STBC [36], BFTEM [24], and the proposed FTBF as the number of FinTechusers scales from 20
to 200. Throughput, measured in Kbps, increases across all frameworks with higher user loads,
demonstrating the scalability of these systems. The results show that EFMCDM starts at
approximately 250 Kbps and reaches 650 Kbps with 200 users, indicating moderate scalability.
STBC performs slightly better, starting at 270 Kbps and peaking at 700 Kbps. BFTEM
outperforms EFMCDM and STBC, achieving 300 Kbps initially and scaling to 750 Kbps.
However, the proposed FTBF framework consistently outperforms all three, starting at 320 Kbps
and achieving 850 Kbps at 200 users. This superior performance is attributed to the optimized
communication protocols and advanced load-handling mechanisms within the FTBF framework,
enabling it to efficiently manage the increased user traffic without significant performance
degradation. The results clearly demonstrate FTBF’s ability to deliver greater throughput,
ensuring its suitability for high-demand environments in the FinTech domain. The findings
emphasize that FTBF supports scalability and provides enhanced performance under high user
loads, making it a robust and efficient solution for modern FinTech applications. These results
validate the framework’s design principles and ability to outperform existing trust algorithms in
throughput performance.
0
10
20
30
40
0 5 10 15 20 25
Computation
Time
(Sec)
Number of Transaction

140
Figure 7: Comparison of throughput for different trust algorithms
Figure 8 compares delayed response times for four algorithms, EFMCDM, STBC, BFTEM, and
the proposed FTBF,against varying FinTech data transmission times. The results indicate that the
proposed FTBF framework demonstrates the lowest delayed response times, making it the most
efficient among the compared algorithms. EFMCDM exhibits the highest delayed response times,
beginning at 4.5 µs and increasing to 6.5 µs as transmission time grows. STBC follows a similar
trend, with delays starting at 4.2 µs and peaking at 6.2 µs. BFTEM offers better performance,
starting at 3.9 µs and reaching 5.8 µs under maximum data transmission conditions. In contrast,
the proposed FTBF framework consistently outperforms the others, achieving the shortest
response times, starting at 3.5 µs and increasing to just 5.5 µs as data transmission time rises. The
lower delayed response times observed with the FTBF framework result from its optimized
communication protocols and adaptive load-balancing mechanisms, which ensure faster and more
reliable communication even under high data transmission loads. These results underscore
FTBF's capability to minimize latency, making it a robust and efficient solution for real-time
FinTech applications. This comparison highlights the superiority of the FTBF framework in
providing faster responses and maintaining reliability in dynamic and high-demand FinTech
environments, showcasing its potential to enhance user experiences and operational efficiency.
Figure 8: Comparison of delay response time
Trust value comparison during payment operations across different trust algorithms such as
MDTEM [26], DREAMS [34], and TM-GT [35] highlight the effectiveness of blockchain-based
ZTA. As shown in Figure 9, the trust evaluation index of the ZTA regularly outperforms the
other techniques, demonstrating that it can ensure data integrity and reliability. Notably, the trust
value of the sent information increases proportionally when the service provider sends more data,
showing the Zero Trust approach's scalability and robustness. The proposed FTBF-based
ZTA greatly enhances the trustworthiness of data transmission by utilizing blockchain
0
200
400
600
800
1000
20 40 80 120 160 200
Throughput
(Kbps)
Number of Fintech users
EFMCDM
STBC
BFTEM
FTBF
0
1
2
3
4
5
6
7
0 2 4 6 8 10
Delayed
response
time
(μs)
Fintech data transmission (x 103 )
EFMCDM
STBC
BFTEM
FTBF

141
technology, ensuring secure and reliable communication even in circumstances with high data
volume.
Figure 9: Trust Value for Payment Comparison
Figure 10 shows that the trust value of the ZTA transfer payments regularly outperforms
existing approaches like MDTEM, DREAMS, and TM-GT. This demonstrates its superior
capacity to maintain data integrity and security throughout payment processes. The results show
that as the volume of information the service provider exchanges increases, the trust value
continuously increases. This trend demonstrates the Zero Trust approach's strength and versatility
in controlling data transfer with high accuracy and confidence. The Zero Trust framework uses
blockchain technology to improve the integrity and transparency of the payment process,
ensuring secure communication even in complicated and large-scale situations. In contrast,
MDTEM, DREAMS, and TM-GT function moderately, but they fail to maintain constant trust
when data transfer volumes increase. However, the FTBF-based ZTA, which can dynamically
validate and protect transactions, makes it a more dependable choice for modern payment
systems, as evidenced by the data presented in Figure 9.
Figure 10: Trust value for transfer payment comparison
Furthermore, comparing the trust value of receiver payments across various trust
algorithms proves its capacity to ensure the reliability and security of payment information
received by end customers; the results show that the Zero Trust architecture's trust value steadily
increases as the volume of payment-related data grows. This trend demonstrates the framework's
ability to sustain high data integrity and trustworthiness levels during receiver-sided interactions.
In comparison, MDTEM, DREAMS, and TM-GT have slower trust value growth, demonstrating
0.5
0.6
0.7
0.8
0.9
1
1000 2000 4000 6000 8000 10000
Trust
value
for
payment
Number of payments
MDTEM
DREAMS
TM-GT
Proposed
0.5
0.6
0.7
0.8
0.9
1
1000 2000 4000 6000 8000 10000
Trust
value
for
transfer
payment
Number of payments
MDTEM
DREAMS
TM-GT
Proposed

142
challenges in effectively handling large-scale, secure data transfer. The proposed FTBF-based
ZTA enables a more transparent and safe payment process, especially on the receiver side, where
confidence is critical. The dynamic verification processes built into blockchain improve its
capacity to adapt to increased transaction volumes while ensuring constant and dependable trust
levels. This positions the Zero Trust method as a more resilient and efficient alternative for
modern payment systems, as seen in Figure.11
Figure 11: Trust Value for Receiver Payment Comparison
Furthermore, the proposed FTBF-based Zero Trust Architecture (ZTA) outperforms MDTEM,
DREAMS, and TM-GT algorithms in achieving superior performance in data delivery success
rates. The FTBF-based approach ensures reliable and secure data delivery, even with increasing
network complexity and transmission loads. Unlike MDTEM, DREAMS, and TM-GT
algorithms, which have moderate success rates, the FTBF-based ZTA integrates blockchain
technology and trust verification mechanisms, reducing data delivery failures and improving
reliability. This is due to the architecture's ability to validate and securely transmit data, ensuring
higher trustworthiness and accuracy in delivery processes. The results in Figure 12 highlight the
FTBF-based ZTA's effectiveness in achieving higher delivery success rates than existing trust
algorithms, solidifying its suitability for secure and efficient payment and communication
systems.
Figure 12: Delivery Success Rate Comparison
5.4. Discussion
The proposed study focuses on FTBF-based ZTAs that employ blockchain technology to improve
trust, reliability, and performance in secure communication and payment systems. The study
shows that the proposed blockchain-based ZTA outperforms existing trust algorithms, including
0.5
0.6
0.7
0.8
0.9
1
1000 2000 4000 6000 8000 10000
TRust
value
for
receive
payment
Number of payments
MDTEM
DREAMS
TM-GT
Proposed

143
MDTEM, DREAMS, and TM-GT,regarding trust value, delivery success rate, and reaction time.
The study emphasises the limits of traditional techniques, which frequently struggle with
scalability, trust validation, and consistency, especially in complicated and high-traffic
environments. The results show that the FTBF-based ZTA overcomes these constraints by
utilizing blockchains' decentralized trust verification techniques. FTBF surpasses MDTEM,
DREAMS, and TM-GT for trust value comparisons, demonstrating its capacity to improve the
trust assessment index as the volume of communicated information grows. This improvement can
be linked to the architecture's capacity to securely verify data and provide transparency, resulting
in a higher level of trust for both service providers and recipients. The comparison results show
that the FTBF-based ZTA is more effective at establishing confidence for payment transfers,
making it a dependable alternative for modern financial systems. Regarding delivery success rate,
the proposed technique outperforms other trust algorithms. Includingblockchain technology into
the FTBF-based ZTA allows for strong validation processes, reducing data loss and boosting
delivery reliability. This is especially significant in high-load transmission settings, where classic
methods decrease performance. FTBF provides an efficient mechanism for safe and trustworthy
communication in payment systems by increasing the delivery success rate. The FTBF-based
ZTA has shown to be a very efficient and dependable solution for improving trust, delivery
success, and reaction times in secure communication and payment systems. The proposed
solution ensures secure, transparent, and high-performance operations by addressing the
constraints of current trust algorithms, making it ideal for use in financial technology and other
essential applications. The findings highlight the transformative power of blockchain-based trust
architectures in tackling issues such as data security, trust management, and performance
optimization.
6. CONCLUSION
The architectural design of FTBF provides a solid and comprehensive solution to the FinTech
sector's security, scalability, and trust concerns. The multi-layered framework promotes
modularity, efficiency, and adaptability. The Transaction Layer securely initializes and encrypts
transactions to ensure data integrity. The Validation Layer ensures transaction validity by
integrating the DCFOM with a hybrid TESSA consensus mechanism, resulting in energy-
efficient and tamper-proof validation. Furthermore, the Trust Management Layer applies Zero
Trust concepts to continuously analyse and update user and node trust scores, improving dynamic
trust assessment and system resilience. Together, these layers provide a safe, transparent, and
scalable approach that addresses significant blockchain limitations while improving trust
management in FinTech applications.The framework supports the next generation of
decentralized financial systems by fostering trust and reducing reliance on centralised authorities.
Its emphasis on transparency, auditability and continuous trust updates establishes a robust and
future-ready foundation for modern Fintech applications. Despite its efficiency, the proposed
model faces limitations due to computational overhead from continuous trust score updates,
which may impact real-time processing in high-throughput environments. An adaptive trust
management framework that dynamically adjusts the frequency of trust score updates based on
system load and network conditions will be introduced in terms of future scope. This approach
would ensure a balance between accuracy and computational efficiency, enabling the system to
maintain its performance under varying operational conditions.
DECLARATIONS
Conflict of Interest
The authors declare no conflict of interest.

144
Author Contribution
Al authors are equally contributed
Funding
No fund was received for this work
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