Научная статья на тему 'FUTURE OF MOBILE CREDIT AGGREGATORS: THE ROLE OF ARTIFICIAL INTELLIGENCE AND BIG DATA'

FUTURE OF MOBILE CREDIT AGGREGATORS: THE ROLE OF ARTIFICIAL INTELLIGENCE AND BIG DATA Текст научной статьи по специальности «Экономика и бизнес»

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Ключевые слова
mobile credit aggregators / artificial intelligence / big data / personalization / credit offers / data analysis / data security / automation / мобильные кредитные агрегаторы / искусственный интеллект / большие данные / персонализация / кредитные предложения / анализ данных / безопасность данных / автоматизация

Аннотация научной статьи по экономике и бизнесу, автор научной работы — E. Ponomarev

This paper explores the key aspects of applying artificial intelligence (AI) and big data in mobile credit aggregators (MCA). It examines the role of AI in automating and personalizing credit offers, as well as the use of big data to enhance analytics and decision-making. It analyzes the future prospects of these technologies in the MCA sector and identifies the main challenges, including data security, legal constraints, and ethical concerns.

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Предварительный просмотрDOI: 10.24412/2500-1000-2024-9-4-96-100
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БУДУЩЕЕ МОБИЛЬНЫХ КРЕДИТНЫХ АГРЕГАТОРОВ: РОЛЬ ИИ И БОЛЬШИХ ДАННЫХ

В данной статье исследуются ключевые аспекты применения искусственного интеллекта (ИИ) и больших данных в мобильных кредитных агрегаторах (МКА). Рассматривается роль ИИ в автоматизации и персонализации кредитных предложений, а также использование больших данных для улучшения аналитики и принятия решений. Анализируются перспективы развития этих технологий в сфере МКА, определяются основные вызовы, включая безопасность данных, правовые ограничения и этические проблемы.

Текст научной работы на тему «FUTURE OF MOBILE CREDIT AGGREGATORS: THE ROLE OF ARTIFICIAL INTELLIGENCE AND BIG DATA»

FUTURE OF MOBILE CREDIT AGGREGATORS: THE ROLE OF ARTIFICIAL INTELLIGENCE AND BIG DATA

E. Ponomarev, bachelor's degree

National Research Lobachevsky State University of Nizhny Novgorod (Russia, Nizhny Novgorod)

DOI:10.24412/2500-1000-2024-9-4-96-100

Abstract. This paper explores the key aspects of applying artificial intelligence (AI) and big data in mobile credit aggregators (MCA). It examines the role of AI in automating and personalizing credit offers, as well as the use of big data to enhance analytics and decision-making. It analyzes the future prospects of these technologies in the MCA sector and identifies the main challenges, including data security, legal constraints, and ethical concerns.

Keywords: mobile credit aggregators, artificial intelligence, big data, personalization, credit offers, data analysis, data security, automation.

The rapid advancement of technology transformed the financial services industry, with mobile credit aggregators (MCA) playing a pivotal role in reshaping how consumers access and compare credit offers. As digital platforms, MCA allow users to evaluate multiple credit options from different providers, streamlining the decision-making process.

The use of artificial intelligence (AI) allows MCA to automate credit evaluations, tailor recommendations, and enhance customer experiences by predicting individual preferences and behaviors. Big data provides MCA with the ability to analyze vast amounts of user information, uncovering insights that help optimize credit offerings and improve risk management. Together, these technologies have the potential to significantly reshape the MCA landscape and drive innova-

tion in both product design and customer interaction. The aim of this article - to analyze the application of AI and big data in MCA and evaluate their impact on personalizing credit services.

The role of AI in MCA and possible challenges

The implementation of AI has increasingly become an integral part across various industries, while playing a significant role in driving innovation and improving operational efficiency. In particular, the financial services sector has embraced AI to automate complex processes, enhance decision-making capabilities [1]. In 2023, the estimated value of AI spending in the financial sector reached over $30 billion, with forecasts predicting it will grow to nearly $100 billion by 2027 (fig. 1).

2023 2024 2025 2026 2027 Fig. 1. Estimated value of the financial sector's AI spending worldwide in 2023, with forecasts

from 2024 to 2027, billion dollars [2]

This signifies a compound annual growth rate of 29%, which evinces a considerable upward trajectory in AI investment within the financial industry. In MCA, the integration of AI plays a crucial role in enhancing the efficiency, accuracy, and personalization of the credit selection process. By automating complex decision-making tasks and utilizing advanced algorithms, AI enables MCA to deliver more tailored and user-friendly experiences for customers seeking credit options.

One of the most significant contributions of AI to MCA is the automation of credit evaluations. Assessing an individual's cre-ditworthiness required manual review by financial institutions, which was time-consuming and often subject to human error or bias. With AI, this process is streamlined through the use of machine learning algorithms that can rapidly analyze vast datasets, including credit scores, payment histories, and spending behaviors [3].

Models based on AI are capable of assessing risk more accurately by identifying patterns that may not be immediately visible to human evaluators. This allows for faster approval times and more precise lending decisions. Credit evaluations can accommodate non-traditional data, such as social media activity or utility payment records, to assess creditworthiness, offering an alternative for individuals with limited or no formal credit history. This expands financial inclusion and enables more consumers to access products through mobile platforms.

Another significant advantage of AI in MCA is the ability to personalize credit offers for individual users. Recommendation engines with AI-technologies use consumer data to understand user preferences, behaviors, and financial needs. This allows MCA to

provide highly customized credit options that align with each user's unique financial situation. Through real-time data analysis, AI can predict what type of loan or credit product a consumer is likely to favor based on past transactions, search queries, or financial habits. A consumer with a history of short-term loans might be offered similar products, while someone with consistent savings behavior could receive suggestions for more advantageous long-term loans with lower interest rates.

Predictive analytics is another area where AI adds value to MCA. By processing historical data and identifying trends, AI models can forecast future credit needs and behavior. This enables MCA to anticipate user demands and provide proactive credit solutions, such as offering pre-approved loans during periods of high demand or flagging potential risks for non-payment before they occur. For financial institutions, predictive analytics offers a competitive advantage by enabling more efficient resource allocation and risk management. By understanding customer behavior trends, lenders can fine-tune their product offerings and marketing strategies to meet the evolving needs of the consumer base. AI enhances fraud detection by monitoring user behavior and transaction patterns. By identifying unusual or suspicious activities, AI systems can alert both the aggregator and the user, providing additional security layers to protect sensitive financial data [4].

While AI offers numerous benefits for MCA, its integration also contains several challenges. One of them is data privacy and security. AI relies on extensive amounts of user data to function effectively, raising concerns about how this data is collected, stored, and used (table 1).

Table 1. AI challenges and limitations in financial sector of MCA

Challenge Description Potential solution

Algorithmic bias. AI models may inherit biases from the training data, resulting in unfair credit or loan decisions. Diverse datasets and regular audits are typically used to minimize bias in AI models.

Ethical issues in decision-making. AI-driven decisions may raise ethical questions about fairness and accountability, particularly in automated lending. Clear ethical principles and regular monitoring of AI systems are established to ensure fairness.

Lack of transparency. It is often difficult to explain how AI makes decisions, which can lead to mistrust among consumers and regulators. Interpretable AI models and transparent decision-making processes are often developed to address this issue.

Despite these constraints, the use of AI in MCA streamlines processes and provides personalized credit options. Through enhanced predictive analytics, it can lead to improved user engagement and greater service efficiency. The integration of AI enables more accurate risk assessments, which helps financial institutions make better-informed lending decisions. It also supports the automation of routine tasks, reducing operational costs and minimizing human error.

The application and potential constraints of big data in MCA

Across many industries, large-scale data has emerged as a key catalyst for innovation, and MCA can benefit from its impact as well. According to statistics [5], the global market size for big data analytics was valued at $307,51 billion in 2023. The vast amounts of data generated by users, combined with the growing capacity for storage and real-time analysis, enables MCA to optimize their services in unprecedented ways.

At the core of big data in MCA is the ability to collect and integrate information from a wide array of sources. MCA gather user data not only from traditional financial metrics like credit scores and income, but also from non-traditional sources, such as social media, online purchasing behavior, geoloca-tion data, and more. This holistic approach to data collection provides a comprehensive picture of the user's financial profile. Integration of this data is essential for building a unified and actionable view of the customer. By combining structured data (e.g., transaction histories) and unstructured data (e.g., social media posts or customer reviews), MCA can leverage more sophisticated analytics to generate insights that were previously unattainable.

One of the primary applications of big data in MCA is enhancing credit risk assessment. Lenders relied on credit scores and income statements to gauge a borrower's ability to repay a loan. Extensive data analytics allow for a deeper and more nuanced evaluation of

risk by taking into account a wider range of factors. Spending habits, employment patterns, and even the sentiment expressed in social media activity can be analyzed to predict a user's creditworthiness. This use of alternative data sources can be particularly beneficial for individuals who lack a traditional credit history, providing them with access to financial services they might not have been eligible for otherwise.

Big data enables real-time credit assessments, allowing MCA to quickly adapt to changing circumstances. For instance, if a user's financial situation changes, such as through a job loss or a significant increase in debt, MCA can adjust their credit offerings accordingly, offering more flexible solutions or providing warnings about potential risks.

Similar to AI, big data is instrumental in the personalization of credit offers for users. By analyzing vast amounts of data from multiple channels, MCA can tailor credit products to meet individual needs. This level of customization improves the user experience, making credit offers more relevant and timely. Complex data sets can be used to track a user's financial behavior over time and predict their future needs. If a user frequently makes large purchases during specific periods, MCA can offer pre-approved credit products that align with those purchasing patterns. Big data analytics can detect life events, such as a job promotion or a new mortgage, which may influence the type of credit products a user might need.

Given the sensitive nature of financial and personal information collected by MCA, it is important to understand potential challenges. In this area, maintaining robust data protection protocols is essential. Breaches or misuse of information can result in severe reputation-al and financial damage to both the aggregator and its users. Data quality issues and regulatory compliances also represent challenges that need to be addressed to ensure the effective implementation of big data in MCA (table 2).

Table 2. Challenges of big data in the financial sector [6, 7]

Challenge Description Solutions

Data quality issues. Inconsistent or incomplete data can lead to incorrect analyses and poor decision-making. Data cleansing protocols and consistent data quality monitoring are implemented to maintain.

Scalability and infrastructure. Handling large volumes of data in real-time requires robust and scalable infrastructure. Cloud-based solutions and advanced infrastructure are often used to handle large data volumes efficiently.

Regulatory compliance. Adhering to global and local data regulations is challenging with increasing data volumes. Regular reviews of data handling processes help ensure compliance with evolving regulations.

Big data can be successfully implemented in MCA, while offering various advantages such as enhancing credit risk assessment, personalizing credit offers, and improving fraud detection. With these advancements come significant challenges related to data privacy, quality, security, and regulatory compliance. As MCA continue to leverage the power of big data, they must address these challenges to fully realize the potential of massive information sets while ensuring responsible data usage.

Development prospects of AI and big data analytics in MCA

The rapid evolution of technology, coupled with the growing demand for digital financial services, suggests that MCA continue to play an increasingly important role in the financial ecosystem. As AI and big data become more deeply integrated into these platforms, the future of MCA appears poised for significant transformation.

Integration with other emerging technologies can enhance their capabilities and improve user experiences. Blockchain could be used to create more transparent and secure credit transactions, enabling users to have greater control over their financial data and reducing the risk of fraud. Blockchain's decentralized nature could also allow for the creation of credit histories that are accessible across different financial institutions, improving the efficiency of credit assessments.

One of the most promising aspects of the future of MCA is their potential to expand financial inclusion. By leveraging AI and big data, MCA can provide access to credit for underserved populations, such as individuals with limited or no formal credit history. The use of alternative data sources, such as utility payments, mobile phone usage, or employment records, will allow these platforms to assess creditworthiness more comprehensive-

ly and offer credit to individuals who may have been excluded by traditional financial institutions [8].

Another potential area of growth is the integration of biometric authentication technologies. As concerns over security and fraud grow, MCA may adopt biometric verification methods, such as facial recognition or fingerprint scanning, to ensure that only authorized users can access credit services. This would enhance security while also providing a more seamless and convenient user experience. The role of the Internet of Things could also become more prominent, as MCA tap into connected devices to gain insights into user behavior. Data from smart devices, such as wearables or home automation systems, could provide additional context for credit evaluations, offering a more holistic view of a user's financial health.

The future of MCA is marked by significant technological advancements and the potential for greater financial inclusion. As AI and big data continue to evolve, these platforms will become more personalized, efficient, and secure, offering users tailored credit solutions that meet their specific needs.

Conclusion

The integration of AI and big data into MCA is transforming the way consumers access and interact with financial services. The ability of AI to automate credit evaluations, personalize recommendations, and enhance fraud detection, combined with the insights derived from massive data sets, can significantly improve the efficiency and user experience of these platforms. As MCA continue to evolve, they will offer increasingly tailored credit solutions, expanding financial inclusion and driving innovation in the credit industry. But the adoption of these technologies also brings challenges. Issues such as data privacy, algorithmic bias, regulatory compliance, and

transparency must be carefully addressed to use of AI and big data will be critical as MCA maintain user trust and meet legal require- seek to leverage these tools for long-term ments. Ensuring the responsible and ethical growth and success.

References

1. Bahoo S., Cucculelli M., Goga X., Mondolo J. Artificial intelligence in Finance: a comprehensive review through bibliometric and content analysis // SN Bus Econ. - 2024. - Vol. 4. -P. 23.

2. Estimated value of the financial sector's artificial intelligence (AI) spending worldwide in 2023, with forecasts from 2024 to 2027 / Statista. - URL: https://www.statista.com/statistics/1446037/financial-sector-estimated-ai-spending-forecast/ (date of application: 11.09.2024).

3. Pshychenko D. Evaluation of the effectiveness of implementing Al-based CRM systems // Инновационная наука. - 2024. - № 7-2/2024. - P. 40-45.

4. Bukhtueva I. The role of ai in financial risk management and fraud detection // ISJ Theoretical Applied Science. - 2024. - Vol. 135. № 07. - P. 65-69.

5. Revenue from big data and business analytics worldwide from 2015 to 2022 / Statista. -URL: https://www.statista.com/statistics/551501/worldwide-big-data-business-analytics-revenue/ (date of application: 16.09.2024).

6. Bobunov A. Adaptation and implementation of international software quality standards in financial institutions // The scientific online journal "Stolypinsky Bulletin". - 2024. - № 9/2024.

7. Shao Z. Big Data Revolution in Finance: Opportunities, Challenges, and Future Trends // Advances in Economics, Management and Political Sciences. - 2022. - Vol. 84. - P. 71-76.

8. Milojevic N., Srdjan R. Prospects of artificial intelligence and machine learning application in banking risk management // Journal of Central Banking Theory and Practice. - 2021. -Vol. 10. № 3. - P. 41-57.

БУДУЩЕЕ МОБИЛЬНЫХ КРЕДИТНЫХ АГРЕГАТОРОВ: РОЛЬ ИИ И БОЛЬШИХ ДАННЫХ

Е. Пономарёв, бакалавр

Национальный исследовательский Нижегородский государственный университет им. Н.И. Лобачевского (Россия, г. Нижний Новгород)

Аннотация. В данной статье исследуются ключевые аспекты применения искусственного интеллекта (ИИ) и больших данных в мобильных кредитных агрегаторах (МКА). Рассматривается роль ИИ в автоматизации и персонализации кредитных предложений, а также использование больших данных для улучшения аналитики и принятия решений. Анализируются перспективы развития этих технологий в сфере МКА, определяются основные вызовы, включая безопасность данных, правовые ограничения и этические проблемы.

Ключевые слова: мобильные кредитные агрегаторы, искусственный интеллект, большие данные, персонализация, кредитные предложения, анализ данных, безопасность данных, автоматизация.

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