Digital and Omnichannel Strategies in Credit Recovery: Challenges and Opportunities for Financial Institutions

Flávia de Moraes Barbosa; Daniel de Souza Valotto

DOI: 10.22167/2675-6528-202602907

Article derived from a Final Course Work (TCC), with content based on the student’s original work and adapted to the editorial format of the E&S Magazine with the support of the ResumeAI tool, an artificial intelligence solution developed by Instituto Pecege for textual synthesis and organization.

Abstract

The research analyzed how digital strategies based on omnichannelity can increase the efficiency of credit recovery in financial institutions. The objective was to analyze how the application of digital strategies based on omnichannelity can contribute to increasing the efficiency of credit recovery processes in financial institutions. With a qualitative, exploratory, and descriptive approach, the study used interviews with professionals from five Brazilian companies in the sector, selected for convenience. Processes prior to the adoption of omnichannelity, technologies employed, integration between channels, and implementation challenges were examined. The results indicated that, previously, collection was centered on telephone contacts, with low data integration and fragmented interactions. The implementation of omnichannelity, with the support of tools such as CRM, message automation, digital portals, chatbots, and artificial intelligence, enabled greater integration between channels such as voice, WhatsApp, SMS, and email. This expanded customer journey visibility and improved interaction management. An increase in credit recovery rates, reduction in operational costs, greater adherence to digital negotiations, and improvement in customer experience were observed. It was concluded that omnichannelity is strategic for the digital transformation of collection, by integrating data and channels and allowing for greater efficiency and personalization. However, technological, regulatory, and information security challenges persist, in addition to the need to balance automation and customer experience.

Keywords: CRM; Customer experience; Digital transformation.

1. Introduction

The current economic scenario in Brazil has been marked by a significant increase in default rates and the democratization of credit access, which poses considerable challenges to financial institutions in managing and recovering granted credits (Sebben, 2020). Recent data from Serasa Experian (2025) indicate that the country is going through one of the most critical periods of default in its history. In September 2025, approximately 30.5% of Brazilian families had overdue bills, registering the highest index since 2010. Among these, 13% declared they were unable to pay their debts, and about 48% remained in default for more than ninety days. The widespread impact of indebtedness is also reflected in the business sector, with about eight million companies registered as defaulting in July 2025.

Beyond economic factors, consumer culture and the increasing use of credit contribute to excessive income commitment and the worsening of household debt. Consumerism, combined with the inadequate use of credit and insufficient financial education, can intensify situations of over-indebtedness, especially when consumption exceeds the individual’s financial capacity (Tuma; Oliveira, 2023). This context reinforces the pressing need for more efficient and innovative strategies for credit recovery.

In this scenario, the integration of different customer touchpoints, combined with the use of digital technologies, emerges as a way to create a more fluid and personalized journey. This approach favors the consumer experience and increases the effectiveness of interactions (Kotler, 2017; Kartajaya & Setiawan, 2021). Digital transformation, according to Vial (2019), transcends the mere acquisition of technologies, encompassing changes in processes, organizational structures, and in the ways of creating value and customer relationship.

Traditionally, credit recovery focused on phone calls, text messages, and emails, with a strong reliance on manual activities and human intervention. This model resulted in channels that operated in isolation, generating information fragmentation, repeated contacts, low visibility of negotiation history, and little personalization of the approach. The mere availability of multiple channels does not guarantee an integrated experience, as omnichannel management presupposes the synergistic administration of channels and contact points, considering their interactions throughout the customer journey (Verhoef, Kannan, and Inman, 2015).

In the context of financial institutions, such limitations hinder the comprehensive monitoring of the trading journey and can compromise the continuity and effectiveness of interactions. The COVID-19 pandemic intensified the need for digitalization of these processes, accelerating technological trends and causing changes in business strategies (Amankwah-Amoah et al., 2021). This stimulated the implementation of self-trading portals, messaging applications, digital agents, and automation tools, highlighting that accounts receivable management requires an innovative model, with process automation, data usage, and a focus on customer experience, to increase the effectiveness of credit recovery (Bono et al., 2023; Cândido, 2026).

Given the complexity of the scenario and the gap in the integration and personalization of traditional collection, it becomes essential to invest in innovation, artificial intelligence, omnichannelity, automation, user experience, and team qualification, ensuring compliance with the General Data Protection Law (Bono et al., 2023; Cândido, 2026). The application of technologies and the diversification of contact channels are crucial factors for expanding negotiation possibilities and, consequently, increasing credit recovery rates. The automation of repetitive tasks, such as sending reminders and generating reports, contributes to operational efficiency and allows professionals to direct their efforts towards higher value-added activities, such as debt negotiation and customer relationship management (Laudon; Laudon, 2020). Based on the above, the objective of this work was to analyze how the application of digital strategies based on omnichannelity can contribute to increasing the efficiency of credit recovery processes in financial institutions.

2. Material and Methods

The present study adopted a predominantly qualitative approach, supported by descriptive quantitative data, to analyze the strategies employed in credit recovery through digital and omnichannel practices. The methodological objective was to describe and interpret the implementation of these strategies in financial institutions and specialized companies, as well as to understand the influence of technological innovation on the efficiency of collection processes and interaction with delinquent clients.

The qualitative component of the research focused on the interpretation of perceptions, experiences, and practices reported by the interviewed experts. The quantitative component, in turn, was restricted to the systematization of responses to closed-ended questions and the characterization of participants and cases, without the purpose of statistical inference or generalization of results to the financial sector.

Regarding the design, a multiple case study, descriptive and exploratory in nature, was conducted. This approach was considered adequate for investigating contemporary phenomena in their real contexts, especially when the boundaries between the phenomenon studied and the organizational environment are not clearly defined (Yin, 2018). Each of the five selected organizations constituted a distinct case.

The units of analysis were the companies and their organizational credit recovery processes, while the units of observation corresponded to the interviewed professionals, their perceptions, and the information provided about the practices adopted. The multiple-case logic allowed for the examination of each organization in its context and, subsequently, for the comparison of cases to identify patterns and divergences (Yin, 2018).

The selection of cases was intentional and guided by specific criteria, not being a probabilistic sampling. Organizations that directly operated in credit granting or recovery, had operations related to financial sector portfolios, and had experience in adopting digital collection strategies were included.

The selected companies were located in São Paulo (SP), Bauru (SP), or Curitiba (PR), and provided professionals with direct knowledge of the phenomenon investigated. The choice sought to include organizations with distinct characteristics of size, location, operating time, and segment, favoring comparison between the studied cases.

Seven specialists linked to the five investigated companies participated in the research. All had experience in managing non-performing portfolios, debt negotiation, or the implementation of technological collection solutions, with time in credit recovery ranging from ten to 25 years, at least five of which were dedicated to digital strategies.

The participants operated with banking portfolios, and the majority had experience in credit recovery operations for individuals. Some also had experience with corporate and retail portfolios, including financing and credit cards, which demonstrates the diversity of the professionals’ activities.

For data collection, semi-structured interviews were conducted, which provided flexibility and depth in the responses. This format allowed for the capture of objective information, as well as specialists’ perceptions and experiences regarding digital credit recovery practices (Kvale & Brinkmann, 2015).

The interview script was based on the methodological principles of Creswell (2014) and structured with eight questions. The questions addressed aspects such as digital transformation, omnichannel, customer experience, channel integration, use of digital technologies, process efficiency, and future trends, based on studies by Verhoef, Kannan, and Inman (2015), Lemon and Verhoef (2016), Laudon and Laudon (2022), Turban et al. (2018), and Kotler, Kartajaya, and Setiawan (2021).

The questions were structured in open and closed formats. The open-ended questions allowed participants to express their experiences, perceptions, and examples related to the application of digital strategies in credit recovery, while the closed-ended ones characterized the professional profile, years of experience, portfolio types, and use of digital tools.

The interviews were conducted individually, via the Microsoft Teams platform or in person, according to the participants’ availability. Each interview lasted approximately 20 to 30 minutes. The responses were recorded through notes taken by the researchers, respecting the flow of the conversation.

After each interview, the notes were organized by case and by question from the script, maintaining the link between the evidence, the participants, and the organizations. Before the interviews, participants were contacted electronically and informed about the research objectives, its academic purpose, how the information would be used, and the confidentiality measures adopted.

Participation was voluntary, with no obligation or financial incentive. Before data collection, participants received and accepted the Informed Consent Form (ICF). Measures were taken to preserve confidentiality and anonymity by identifying organizations with fictitious names (Companies A, B, C, D, and E) and omitting specific names and positions.

The data analysis was performed in two complementary stages: intracasos analysis and intercases analysis. In the first, the evidence from each company was examined separately, considering its context and digital practices. In the second, the five cases were systematically compared to identify recurring patterns and specific practices (Yin, 2018).

For the treatment of open-ended responses, content analysis was used, a technique that allows for the systematic organization, interpretation, and assignment of meaning to communications (Bardin, 2016). The process comprised three phases: pre-analysis, material exploration with coding and classification of responses, and treatment of results with inference and interpretation.

The registration units were constituted by excerpts of the answers related to the research objectives. The analytical categories were defined from the study objectives and adjusted during the reading of the material, including digital transformation, channel integration, automation, artificial intelligence, user experience, technological challenges, data protection, and improvement opportunities.

For each category, a comparative matrix was developed containing the evidence identified in the companies. The answers to the closed-ended questions were submitted to simple descriptive statistics, using absolute and relative frequencies, to characterize the participants and complement the qualitative interpretation, without the purpose of statistical inference.

In the final interpretation, three sources were articulated: the evidence from each case, the comparison between cases, and the theoretical framework. This analytical triangulation sought to verify the coherence of the interpretations with the obtained reports and with the concepts discussed in the literature, representing the participants’ perceptions of organizational practices.

In the bibliographic survey and initial organization of information, artificial intelligence tools, such as ChatGPT, were employed to support the identification of keywords, the initial location of references, and the preliminary structuring of themes. These tools were not used as sources of data or scientific evidence, and the generated content was critically analyzed by the researchers.

In the analysis stage, artificial intelligence resources were used only to support textual organization and review, without autonomously performing the coding, categorization, or interpretation of the interviews. Analytical decisions remained the responsibility of the researchers, and no data that could identify participants or organizations was entered into public artificial intelligence tools.

The present study adopted a predominantly qualitative approach, supported by descriptive quantitative data, to analyze the strategies employed in credit recovery through digital and omnichannel practices. The methodological objective was to describe and interpret the implementation of these strategies in financial institutions and specialized companies, as well as to understand the influence of technological innovation on the efficiency of collection processes and interaction with delinquent clients.

The qualitative component of the research focused on the interpretation of perceptions, experiences, and practices reported by the interviewed experts. The quantitative component, in turn, was restricted to the systematization of responses to closed-ended questions and the characterization of participants and cases, without the purpose of statistical inference or generalization of results to the financial sector.

Regarding the design, a multiple case study, descriptive and exploratory in nature, was conducted. This approach was considered adequate for investigating contemporary phenomena in their real contexts, especially when the boundaries between the phenomenon studied and the organizational environment are not clearly defined (Yin, 2018). Each of the five selected organizations constituted a distinct case.

The units of analysis were the companies and their organizational credit recovery processes, while the units of observation corresponded to the interviewed professionals, their perceptions, and the information provided about the practices adopted. The multiple-case logic allowed for the examination of each organization in its context and, subsequently, for the comparison of cases to identify patterns and divergences (Yin, 2018).

The selection of cases was intentional and guided by specific criteria, not being a probabilistic sampling. Organizations that directly operated in credit granting or recovery, had operations related to financial sector portfolios, and had experience in adopting digital collection strategies were included.

The selected companies were located in São Paulo (SP), Bauru (SP), or Curitiba (PR), and provided professionals with direct knowledge of the phenomenon investigated. The choice sought to include organizations with distinct characteristics of size, location, operating time, and segment, favoring comparison between the studied cases.

Seven specialists linked to the five investigated companies participated in the research. All had experience in managing non-performing portfolios, debt negotiation, or the implementation of technological collection solutions, with time in credit recovery ranging from ten to 25 years, at least five of which were dedicated to digital strategies.

The participants operated with bank portfolios, and the majority had experience in credit recovery operations for individuals; four also had experience with corporate portfolios; and three reported operating in retail portfolios, including financing, credit cards, and other financial products.

For data collection, semi-structured interviews were conducted, which provided flexibility and depth in the responses. This format allowed for the capture of objective information, as well as specialists’ perceptions and experiences regarding digital credit recovery practices (Kvale & Brinkmann, 2015).

The interview script was based on the methodological principles of Creswell (2014) and structured with eight questions. The questions addressed aspects such as digital transformation, omnichannel, customer experience, channel integration, use of digital technologies, process efficiency, and future trends, based on studies by Verhoef, Kannan, and Inman (2015), Lemon and Verhoef (2016), Laudon and Laudon (2022), Turban et al. (2018), and Kotler, Kartajaya, and Setiawan (2021).

The questions were structured in open and closed formats. The open-ended questions allowed participants to express their experiences, perceptions, and examples related to the application of digital strategies in credit recovery, while the closed-ended ones characterized the professional profile, years of experience, portfolio types, and use of digital tools.

The interviews were conducted individually, via the Microsoft Teams platform or in person, according to the participants’ availability. Each interview lasted approximately 20 to 30 minutes. The responses were recorded through notes taken by the researchers, respecting the flow of the conversation.

After each interview, the notes were organized by case and by question from the script, maintaining the link between the evidence, the participants, and the organizations. Before the interviews, participants were contacted electronically and informed about the research objectives, its academic purpose, how the information would be used, and the confidentiality measures adopted.

Participation was voluntary, with no obligation or financial incentive. Before data collection, participants received and accepted the Informed Consent Form (ICF). Measures were taken to preserve confidentiality and anonymity by identifying organizations with fictitious names (Companies A, B, C, D, and E) and omitting specific names and positions.

The data analysis was performed in two complementary stages: intracasos analysis and intercases analysis. In the first, the evidence from each company was examined separately, considering its context and digital practices. In the second, the five cases were systematically compared to identify recurring patterns and specific practices (Yin, 2018).

For the treatment of open-ended responses, content analysis was used, a technique that allows for the systematic organization, interpretation, and assignment of meaning to communications (Bardin, 2016). The process comprised three phases: pre-analysis, material exploration with coding and classification of responses, and treatment of results with inference and interpretation.

The registration units were constituted by excerpts of the answers related to the research objectives. The analytical categories were defined from the study objectives and adjusted during the reading of the material, including digital transformation, channel integration, automation, artificial intelligence, user experience, technological challenges, data protection, and improvement opportunities.

For each category, a comparative matrix was developed containing the evidence identified in the companies. The answers to the closed-ended questions were submitted to simple descriptive statistics, using absolute and relative frequencies, to characterize the participants and complement the qualitative interpretation, without the purpose of statistical inference.

In the final interpretation, three sources were articulated: the evidence from each case, the comparison between cases, and the theoretical framework. This analytical triangulation sought to verify the coherence of the interpretations with the obtained reports and with the concepts discussed in the literature, representing the participants’ perceptions of organizational practices.

In the bibliographic survey and initial organization of information, artificial intelligence tools, such as ChatGPT, were employed to support the identification of keywords, the initial location of references, and the preliminary structuring of themes. These tools were not used as sources of data or scientific evidence, and the generated content was critically analyzed by the researchers.

In the analysis stage, artificial intelligence resources were used only to support textual organization and review, without autonomously performing the coding, categorization, or interpretation of the interviews. Analytical decisions remained the responsibility of the researchers, and no data that could identify participants or organizations was entered into public artificial intelligence tools.

3. Results and Discussion

The analysis of digital strategies and omnichannel approaches in credit recovery in financial institutions revealed a significant transition from traditional models to more integrated and technologically advanced approaches. Initially, collection operations were predominantly focused on the voice channel, with fragmented interactions and low data integration, which limited customer journey visibility and communication personalization. This traditional setup, observed in the five companies studied, hindered the efficiency and effectiveness of credit recovery processes, as per the research problem that motivated this study.

The advent of the COVID-19 pandemic, in March 2020, acted as a catalyst for the acceleration of demand for digital interactions, driving a remarkable advancement in the technologies employed. Companies were compelled to adapt quickly to this new scenario, seeking solutions that would allow for the continuity of operations and the maintenance of contact with clients in a remote work environment with physical contact restrictions. This forced change highlighted the need for a more robust and integrated digital infrastructure for debt management.

Before Omnichannel: The Traditional Credit Recovery Scenario

In the period prior to the full adoption of omnichannel strategies, companies A, B, C, D, and E were characterized by credit recovery processes that relied heavily on telephone contact. Other digital channels were used sporadically and in an unintegrated manner, resulting in inefficient information management and an inconsistent customer experience. Company A, for example, with 55 years of operation in banking credit, faced fragmented interactions and little visibility into the customer journey, which compromised the effectiveness of debt negotiations.

Since the pandemic, Company A has expanded its actions to include SMS and email, in addition to creating a self-service portal, allowing customers to access the website to negotiate their debts autonomously. This initiative reduced the dependence on human operators and marked the beginning of greater digitalization. In mid-2021, WhatsApp Business was incorporated as the main communication tool, using standardized templates and in compliance with the General Data Protection Law (LGPD), which demonstrates a growing concern for data security and regulation.

In 2022, Company A introduced virtual negotiating agents based on decision trees, expanding the participation of digital contact, whether via WhatsApp or voice. Although these implementations have generated an increase in digital expansion, the company does not yet have its databases 100% integrated, which prevents a fully unified experience. This limitation reflects a common challenge in digital transformation, where the integration of legacy systems with new technologies is a complex and continuous process, as pointed out by Vial (2019).

Company B, a large financial institution with 25 years of operation, also relied on sporadic phone calls and emails, resulting in low contact recurrence and difficulty in measuring the complete customer journey history. The pandemic drove investments in technology, leading to the implementation of SMS and WhatsApp messaging automation tools between 2020 and 2024, integrated with a CRM and an orchestrator with BI analytics for customer prioritization. The company began, in 2025, an ambitious project to integrate all its databases, aiming for a 100% omnichannel approach by the end of 2026, demonstrating a commitment to digital maturity.

Company C, a medium-sized enterprise with 28 years of experience, used digital channels in a limited way, with communication dependent on human agents, which generated difficulties in scalability and personalization. Between 2020 and 2024, the company advanced technologically, creating its own orchestrator in 2022 to define collection strategies. In mid-2021, it adopted self-service portals and, in 2023, began using decision tree-based chatbots, integrated with internal systems to consolidate customer history. In 2024, it started creating virtual agents with AI for smaller portfolios, although the CRM does not yet record all digital conversations and the databases are not fully integrated.

Company D, a large financial institution with 40 years of operation, already had partially digitized processes, but still focused on phone calls and scattered data. The pandemic also led it to reformulate its tools and implement a high-tech orchestrator to optimize strategy definition. In 2022, it introduced virtual voice agents and decision-tree-based chatbots, which, despite being limited, increased the scale of service and reduced the volume of demands for human operators. In 2025, there was a significant advance with artificial intelligence-based chatbots, capable of understanding natural language, although still in the testing phase for customers with lower default rates. In 2026, the company plans a more strategic approach, with continuous curation and monitoring of AI agents for greater accuracy and personalization.

Company E, with 15 years of experience in retail and wholesale credit, has structured its processes with a focus on WhatsApp, a digital portal, SMS, and automated alerts, all integrated into a centralized CRM. Previously, it relied on phone calls and emails, with low personalization and manual recording. After implementing digital solutions, the company observed a 25% growth in digital negotiation adoption, a reduction in cost per recovery, and an increase in operational efficiency. However, it still faces limitations in personalizing interactions and integrating with voice channels, similar to Company C’s scenario.

The transition to omnichannel and its impacts on institutions

The research results demonstrate that omnichannelity has evolved from a complementary feature to a central and structuring role in the collection processes of the analyzed companies. All organizations investigated indicated a clear transition from fragmented models to more integrated strategies, in which customer experience and operational efficiency are addressed jointly. This shift reflects the understanding that the mere availability of multiple channels does not guarantee an integrated experience, making the synergistic management of contact points essential, according to Verhoef, Kannan, and Inman (2015).

When analyzing each company, it was found that the integration of channels such as telephone, WhatsApp, SMS, and digital portal resulted in concrete performance gains. On average, respondents reported a 15% increase in credit recovery through digital and human channels, according to their performance controls. Company A, for example, achieved a 15% increase in credit recovery effectiveness, with greater engagement in digital channels and a reduction in unnecessary phone calls, which was the result of a comparative analysis of performance indicators before and after the implementation of omnichannel strategies.

Company B, in turn, registered a 20% increase in digital credit recovery, in addition to a significant reduction in operational costs and greater assertiveness in customer prioritization. The improvement in customer experience was notable, allowing interactions initiated by email or digital portal to be completed via WhatsApp, ensuring continuity of service. These findings are aligned with the literature, which emphasizes the importance of omnichannelity to keep up with the new consumer profile, who seeks convenience, personalization, and fluidity in interactions (Kotler et al., 2021).

Company C, with the implementation of chatbots and digital portals, observed a reduction of up to 20% in negotiation time and an increase in self-service rate. However, full integration with other channels, such as voice and email, still represents a challenge, limiting the potential for a completely unified journey. Company E also reported a 15% growth in adherence to digital negotiations, with a reduction in cost per recovery and an increase in operational efficiency, although it still faces limitations in personalizing interactions and integrating with voice channels.

The integration between digital and human channels in the recovery journey

The research showed that the integration between digital and human channels increases the effectiveness of credit recovery, with WhatsApp standing out as the channel with the highest adherence and conversion. At Company A, digital agents on WhatsApp facilitate inquiries and simulations, reducing the need for phone calls. Company B prioritizes higher-risk clients and forwards only complex cases for human assistance, optimizing resource use. At Company C, chatbots handle short delays, while the digital portal supports self-service for longer delays, offering customer flexibility.

Company D uses virtual agents to manage complete flows, ensuring continuity between voice and text, which demonstrates an advanced level of orchestration. Company E, on the other hand, combines automated alerts with WhatsApp inquiries, serving customers with a higher average ticket value. These findings corroborate the literature, which highlights the dependence of omnichannel maturity in the financial sector on technological integration and the adaptation of channels to customer behavior (Wyrzykowska et al., 2024). Furthermore, omnichannel solutions allow for the personalization of collection interactions, adjusting the channel and agent type according to the customer’s profile and risk, thereby increasing engagement and conversion (Nandipati, 2024).

The impact of integration varies according to the customer profile and the debt delay range. Virtual agents tend to be more efficient in shorter delays, where the customer has greater ease in understanding the amounts owed. In contrast, self-service and negotiations via WhatsApp show better results in longer delay ranges, as they offer greater autonomy and comfort for the customer. At Company A, for example, automated interactions work better in the initial stages, when the customer is more prone to resolve the issue quickly, while digital channels that allow greater autonomy are more accepted in prolonged situations.

Company B demonstrates actions aligned with these different moments, directing rapid and automated approaches for initial contacts and reserving more flexible interactions for cases requiring greater sensitivity. At Company C, although digital solutions contribute to initial contacts, human support still plays a more prominent role in complex cases. It was observed that, on average, 15% of customers initially served by voice migrate to text channels, and these channels receive about 20% of interactions organically, concentrating negotiations with higher average ticket values.

The analysis of the results also revealed the need for greater curation and testing in virtual voice agents so that they become more efficient in long delays, where conversational ability is crucial. This continuous adaptation of digital agents and the personalization of approaches are fundamental to optimizing credit recovery and customer experience. The ability to transition between different channels without losing the history of previous interactions improves the customer experience, reducing friction and making communication more coherent throughout the negotiation journey.

Personalized engagement is enabled by the consolidation of data in CRM systems and analytical databases, allowing messages, offers, and contact channels to be adapted according to specific customer characteristics, such as payment history, delinquency range, or behavior in previous interactions. Integrated communication results from the centralized orchestration of channels, which operate in a coordinated manner within the same relationship strategy, avoiding redundant contacts and contributing to a more organized and efficient approach. This integrated view of interactions allows companies to analyze behavioral patterns, identify channel preferences, and better understand the motivations associated with non-payment.

The increase in conversion rates occurs mainly due to the combination of automation, digital channels, and self-service options. Digital negotiation platforms and instant messaging applications allow customers to resolve their financial situation quickly and conveniently, increasing the chances of debt regularization. Customer loyalty and retention also emerge as relevant benefits, as less invasive and more transparent approaches contribute to preserving the relationship between company and consumer, even in cases of default. Data-driven decision-making, a pillar of the omnichannel strategy, feeds analytical models and artificial intelligence systems, allowing for the adjustment of communication strategies and improvement in the assertiveness of collection actions.

Challenges and future trends in digital credit recovery

The implementation of omnichannel, although strategic for operational efficiency and enhancement of the customer experience, involves significant structural challenges. One of the main ones is the integration between systems and databases, which requires technological investments and standardization efforts to consolidate a unified customer view. Interoperability between CRM platforms, legacy systems, digital communication tools, and analytical databases is complex, and technical limitations, especially via APIs, hinder the full orchestration of contact channels.

Another recurring aspect is the regulatory demands and restrictions imposed by digital platforms, particularly in the use of messaging applications. The interviewees highlighted that usage rules and communication policies can limit activation strategies, requiring additional care to avoid invasive or inappropriate approaches. An interviewee from Company C emphasized: “Our biggest challenge is WhatsApp’s regulatory norms, which, for the most part, are not geared towards the scope of Collection; we are extremely careful not to infringe the activation regulations and to ensure this channel does not cause discomfort, so that we do not have significant impacts on our lines.”

Issues related to information security and fraud risk also emerge as relevant challenges. The increased use of digital channels heightens the need for robust authentication mechanisms, data protection, and assurance of communication legitimacy, especially in contexts where the customer may exhibit distrust towards messages received through digital channels. The adaptation of customers traditionally linked to in-person or telephone models is also a challenge, as, although digital channels expand scale and efficiency, there are consumer segments that resist automated platforms or distrust digital interactions.

In this context, companies need to develop strategies that reconcile technological innovation with building trust and transparency in the relationship, seeking a balance between operational efficiency and customer experience. The automation of collection processes can increase productivity, but if poorly calibrated, it can generate perceptions of excessive or invasive approach. Thus, it is essential to develop strategies that consider not only efficiency but also the quality of the customer experience throughout the negotiation process, according to the literature on customer experience (Lemon; Verhoef, 2016).

Despite these challenges, the results indicate important trends in the evolution of omnichannel strategies in credit recovery. The growing prominence of data usage and analytical technologies to support decision-making and interaction personalization stands out. The consolidation of integrated information bases, associated with the use of predictive algorithms, allows for the identification of behavioral patterns and more assertive adjustment of communication strategies. In this scenario, the expansion of artificial intelligence use is observed, especially through virtual agents and automated negotiation systems.

These technologies have been used mainly in mass operations and in shorter delay ranges, where the volume of customers is high and automation allows for significant scale gains. An interviewee from Company D stated: “AI has already been a strong trending technology for personalizing negotiation, meaning, positive impacts. Regarding negative impacts, we have WhatsApp regulation and security issues (fraud).” This perspective highlights the potential of AI to optimize personalization, while also pointing to the regulatory and security concerns that accompany its implementation.

In summary, the adoption of omnichannel has generated clear benefits for credit recovery operations, such as increased efficiency and improved communication, but its consolidation depends on continuous adjustments, investment in technology, and, mainly, a keen focus on the customer experience throughout the entire journey. Companies A, B, C, D, and E, although in different stages of digital maturity, demonstrate that omnichannel is a strategic concept in digital transformation, fundamental for customer experience and operational efficiency, according to Chandratreya’s theory (2024) and the impacts described by Ristevska-Jovanovska (2026).

4. Conclusion

This study sought to analyze how the application of digital strategies based on omnichannelity can contribute to increasing the efficiency of credit recovery processes in financial institutions. It was found that, before the implementation of these strategies, collection operations were predominantly centered on telephone contacts, characterized by fragmented interactions and low data integration, which limited customer journey visibility. With the adoption of omnichannelity, driven by tools such as CRM, message automation, digital portals, chatbots, and artificial intelligence, a significant integration between channels such as voice, WhatsApp, SMS, and email was observed. This transition expanded customer journey visibility and improved interaction management, resulting in an average increase of 15% in credit recovery rates, reduction in operational costs, greater adherence to digital negotiations, and a notable improvement in customer experience. Omnichannelity, therefore, emerges as a strategic pillar for the digital transformation of collection, by integrating data and channels and enabling greater efficiency and personalization of approaches.

However, the implementation of omnichannel still faces structural challenges, such as the complex integration between systems and databases, the regulatory requirements of digital platforms, and concerns about information security and fraud risk. Furthermore, the adaptation of customers traditionally linked to in-person or telephone models and the need to balance automation with a humanized customer experience represent critical points. It is suggested that future studies deepen these findings through research with larger samples and quantitative analyses, exploring the optimization of curation and testing of voice virtual agents for long delays, and investigating strategies to mitigate regulatory and security challenges. The consolidation of omnichannel as a strategic element requires continuous adjustments, investments in technology, and a keen eye on the customer experience throughout the entire credit recovery journey.

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Article originating from the Final Course Work of Specialization in Digital Business from the MBA USP/Esalq

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