Anchored on the Resource-Based View (RBV), this study examined the impact of AI-driven solutions in

financial technology on service delivery, focusing on fraud detection, credit risk assessment, and personalized

advisory systems in seven insurance firms in Lagos State. Using a quantitative approach, data were collected

from 304 respondents selected through stratified random sampling. Descriptive statistics and regression analysis

were employed to test the hypotheses.

Findings revealed that AI-driven fraud detection (β = 0.352, p < 0.01), AI-powered credit risk assessment (β =

0.294, p < 0.01), and AI-enabled personalized advisory systems (β = 0.301, p < 0.01) each exerted significant

positive effects on service delivery. Descriptive results showed high mean scores across variables (≥ 4.0),

Lagos State

The rapid growth of financial technology (FinTech) has transformed the landscape of service delivery across

industries, with the insurance sector being one of the major beneficiaries (Mogaji et al., 2020). Artificial

intelligence (AI) has emerged as a powerful driver of this transformation, enabling firms to optimize processes,

detect anomalies, and provide highly customized solutions that align with customer expectations (Karnati, 2025).

In today’s competitive financial environment, organizations are under constant pressure to deliver efficient

services while minimizing risks and maximizing customer trust (Hentzen et al., 2021). This has increased the

importance of AI-driven innovations in strengthening fraud detection, enhancing credit risk management, and

personalizing advisory services to improve overall service quality (Daiya, 2024).

cornerstone of operational resilience and a crucial determinant of efficient service delivery (Anugu, 2025).

Similarly, credit risk assessment has undergone a major shift with the integration of AI solutions (Kagalwala et

al., 2025). Traditional methods often relied heavily on manual evaluation and historical data, which could be

prone to delays and inaccuracies (Huang & Rust, 2020). In contrast, AI-powered systems provide predictive

analytics and real-time risk profiling, allowing insurers to evaluate customer creditworthiness with greater

precision and speed (Komati, 2025). This has significant implications for service delivery, as faster and more

accurate assessments reduce delays in policy issuance and claims processing (Mogaji et al., 2022). The ability

to make quick yet reliable decisions is central to maintaining competitiveness in a sector that depends heavily

on client trust and satisfaction (Deepika, 2025).

In addition, the rise of AI-enabled personalized advisory systems has redefined customer engagement in the

The insurance industry in Lagos State, being the financial hub of Nigeria, has embraced these AI-driven

innovations at a faster pace compared to other regions. The highly competitive environment and the diverse

needs of customers in this market demand greater efficiency and service quality from insurers (Lomas et al.,

2024). Firms that leverage AI for fraud detection, credit risk assessment, and advisory systems are better

positioned to improve their service delivery and retain customer trust (Ashrafuzzaman et al., 2025). Thus, the

impact of AI-driven solutions on service delivery is not only a matter of technological adoption but also a critical

factor for long-term survival and growth in the insurance industry (Ionescu & Diaconita, 2023).

Lagos State represents Nigeria’s economic and financial nerve center, accounting for a substantial share of the

nation’s insurance and fintech activities. Its dense urban population, dynamic market conditions, and progressive

regulatory environment led by institutions such as the National Insurance Commission (NAICOM) create both

and concerns about cybersecurity and data privacy. Additionally, the customer base in Lagos is highly diverse

ranging from digitally literate, tech-savvy clients to traditionally underserved populations which places pressure

on insurers to offer adaptable and inclusive service delivery models.

These contextual realities make Lagos State a uniquely strategic and complex setting for examining the

relationship between AI-driven financial technologies and service delivery. Understanding how firms navigate

these dynamics provides deeper insight into both the operational and strategic implications of AI adoption in

emerging markets, where technological advancement often outpaces regulatory and infrastructural readiness.

Despite the increasing adoption of AI-driven solutions in the financial services industry, many insurance firms

in Lagos State still struggle with issues related to service delivery (Agu et al., 2024). Fraudulent claims continue

to cost insurers substantial amounts of money each year, eroding profits and undermining customer confidence.

Furthermore, the lack of effective personalized advisory systems limits the ability of insurers to meet the diverse

needs of their clients in a timely and efficient manner (Cao, 2021). Customers increasingly expect services

tailored to their individual circumstances, but many firms are yet to deploy AI systems capable of providing real-

time and relevant recommendations (Jose & Jose, 2024). These challenges emphasize the need to examine how

AI-driven solutions in fraud detection, credit risk assessment, and personalized advisory systems influence

service delivery in insurance firms operating in Lagos State.

The purpose of this study is to investigate the Impact of AI-Driven Solutions in Financial Technology on Service

Delivery: A Study of Fraud Detection, Credit Risk Assessment, and Personalized Advisory Systems in Insurance

Firms in Lagos State. Specifically, the research aims to examine the impact of AI-driven fraud detection systems

The following hypotheses guide this study:

H₁: AI-driven fraud detection systems have a significant impact on service delivery in insurance firms in Lagos

State.

H₂: AI-powered credit risk assessment tools have a significant effect on service delivery in insurance firms in

Lagos State.

H₃: AI-enabled personalized advisory systems have a significant influence on service delivery in insurance firms

improving service delivery outcomes. When effectively deployed, AI capabilities satisfy the VRIN criteria: they

are valuable (enhance efficiency and accuracy), rare (not universally available across firms), difficult to imitate

(due to proprietary data and algorithms), and non-substitutable (no equivalent resource provides the same level

of intelligence or automation).

Thus, the RBV provides an explanatory lens for understanding why AI-driven innovations lead to improved

service delivery beyond the what captured by statistical results. It highlights that firms which integrate AI more

effectively into their processes gain a sustainable advantage through improved productivity, reliability, and client

satisfaction.

relationships in the operational performance of the firms while enabling regression testing of hypotheses.

The population comprised 1,271 staff members across seven insurance firms in Lagos State: AIICO Insurance

Plc, Leadway Assurance, AXA Mansard, Cornerstone Insurance, Heirs Insurance Group, Consolidated Hallmark

Insurance Plc, and Regency Alliance Insurance Plc. These staff members included managerial, operational, and

IT personnel involved in AI implementation and service delivery. The population represented individuals

directly exposed to fraud detection, credit risk assessment, and personalized advisory processes, making them

appropriate respondents. Limiting the scope to these firms provided practical feasibility while ensuring relevance,

as these organizations were leading adopters of AI-driven financial technology in Lagos State.

for data collection, ensuring the study’s findings could be generalized to the broader population of staff in these

firms.

The Taro Yamane’s (1967) formula for the determination of the sample size is:

n N

1+N(e)

Where:

n = sample size

N = population size (1,271)

1+1,271(0.0025)

n = 1,271

1+3.1775

n = 1,271

4.1775

n = 304

Data were collected using a structured questionnaire with closed-ended items rated on a four-point Likert scale

(strongly agree, agree, disagree, strongly disagree). The questionnaire captured perceptions of AI-driven fraud

detection, credit risk assessment, personalized advisory systems, and service delivery. This instrument was

appropriate because it standardized responses, minimized ambiguity, and facilitated quantitative analysis. Items

Control variables included firm size, age, and IT budget. Each variable was quantified using standardized indices

derived from questionnaire responses, facilitating regression analysis.

Respondents agreed that AI-enhanced processes improved customer satisfaction, reduced errors, and accelerated

turnaround times. The alignment between AI solutions and service quality suggests a synergistic effect, as

improved fraud detection, credit assessment, and personalized advisory directly influence operational efficiency.

These findings are supported by Huang & Rust (2020), Komati (2025), and Mogaji et al. (2022), who argued

that AI adoption in financial services enhances customer experience, streamlines operations, and strengthens

trust, highlighting the integral role of AI in shaping high-quality service delivery.

Table 5: Regression Model Summary and Goodness-of-Fit

The regression model indicated that AI-driven solutions collectively explained approximately 61% of the

Table 6: Regression Analysis - Impact of AI-driven Fraud Detection on Service Delivery

The regression results indicated that AI-driven fraud detection had a significant positive effect on service

delivery (β = 0.352, p < 0.01). This demonstrates that higher fraud detection accuracy and faster response

substantially enhance customer satisfaction and reduce operational errors. The findings support prior research

by Khan et al. (2024), Anugu (2025), and Cao (2021), which emphasized that AI fraud detection tools mitigate

risks, protect revenue, and contribute directly to the efficiency and reliability of financial services. It underscores

the necessity for insurance firms to invest in robust AI fraud detection systems to sustain high-quality service

delivery.

Table 7: Regression Analysis - Impact of AI-powered Credit Risk Assessment on Service Delivery

The results revealed that AI-powered credit risk assessment significantly influenced service delivery (β = 0.294,

The analysis showed that AI-enabled personalized advisory significantly impacted service delivery (β = 0.301,

p < 0.01). Personalized recommendations, high engagement, and accurate product suggestions contributed to

higher satisfaction and retention. These results corroborate the studies of Ashrafuzzaman et al. (2025),

Egbuhuzor et al. (2025), and Hentzen et al. (2021), emphasizing that AI-driven personalization enhances

customer-centric services, strengthens client relationships, and improves operational outcomes. Consequently,

implementing sophisticated AI advisory systems is essential for insurance firms aiming to boost service delivery

and maintain competitive advantage in Lagos State.

and Adegbite (2025), who emphasized that AI fraud detection mitigates financial risks, strengthens trust, and

enhances overall service quality in financial institutions.

The second objective focused on the effect of AI-powered credit risk assessment on service delivery. The mean

scores showed strong agreement among respondents regarding the accuracy of credit scoring, default rate

reduction, and rapid decision-making, with a grand mean of 4.08. Regression analysis revealed a significant

positive influence on service delivery (β = 0.294, p < 0.01), confirming the alternative hypothesis. These results

suggest that AI-enabled credit assessment systems enable faster and more precise evaluation of client risk,

reducing delays in policy issuance and enhancing customer trust. The findings are consistent with Daiya (2024),

Adeyeri (2024), and Deepika (2025), highlighting AI’s ability to streamline risk management, optimize resource

allocation, and improve operational efficiency in insurance firms.

personalized AI advisory tools enhance client experience by delivering tailored recommendations, fostering

higher adoption of products, and increasing customer retention. This aligns with Ashrafuzzaman et al. (2025),

Egbuhuzor et al. (2025), and Hentzen et al. (2021), demonstrating that AI-driven personalization strengthens

customer-centric services and contributes substantially to service delivery in the insurance sector.

capability is associated with approximately a 35% enhancement in service quality indicators such as customer

satisfaction, turnaround time, and accuracy. Similarly, improvements in credit risk assessment and personalized

advisory systems yield proportional gains in efficiency and client experience. These magnitudes highlight that

even incremental progress in AI capability can produce substantial operational improvements for insurance firms.

Despite its methodological robustness, this study is subject to certain limitations that should guide interpretation

and future research efforts.

First, the study’s cross-sectional design restricts causal inference, as data were collected at a single point in time.

Longitudinal studies could provide deeper insights into how the effects of AI adoption evolve over time. Second,

the research relied on self-reported data from employees rather than objective operational metrics such as claim

data ethics, and organizational change management. Future researchers could adopt mixed-method or case study

designs to capture the “how” and “why” behind the observed quantitative effects.

The study concluded that AI-driven solutions in financial technology significantly enhance service delivery in

insurance firms in Lagos State. Fraud detection systems were found to reduce operational errors and mitigate

financial risks, while AI-powered credit risk assessment improved decision accuracy and turnaround time.

Personalized advisory systems enhanced customer engagement, satisfaction, and retention. Collectively, these

AI applications contributed to more efficient, reliable, and client-centered service delivery. The regression

analyses confirmed the positive and significant effects of all three AI solutions, validating the study’s hypotheses

and highlighting the transformative role of AI in modern financial services.

The study’s findings offer several practical implications for insurance firms and policymakers. First,

organizations should prioritize data quality management and the integration of interoperable AI systems that

locally relevant and socially responsible.

Through these targeted actions, insurance firms in Lagos and other emerging markets can maximize the

competitive advantage and service excellence that AI-driven financial technologies offer.