Leveraging Digital Technologies to Enhance Business Resilience in Risk Management

Abstract

In today's fast-paced and ever-evolving business landscape, effective risk management and pricing strategies have become crucial for organisations to stay competitive and maximise profitability. In order to effectively manage risks, identify possible threats, 250 and seize opportunities, businesses need to take a proactive and holistic approach to risk management. The current business landscape has left businesses of any size or type, anywhere in the world, facing a wide range of risks which could cause them long-term harm, from financial penalty to reputational damage. Through a systematic review of the literature, this book chapter delved into the ways in which technology is being leveraged to ensure business resilience in risk management, exploring insights from different perspectives and providing an in-depth understanding of the options available. This book chapter also proposed strategic approaches to leveraging and integrating these technologies and innovations into effective risk management and ensure business continuity and resilience.

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Chapter 10
Leveraging Digital
Technologies to Enhance
Business Resilience in
Risk Management
Bronson Mutanda
https:// orcid .org/ 0000 - 0003 - 3438 - 2126
Manicaland State University of Applied Sciences, Zimbabwe
Admire Mtombeni
https:// orcid .org/ 0009 - 0000 - 2439 - 302X
Manicaland State University of Applied Sciences, Zimbabwe
Julius Tapera
https:// orcid .org/ 0000 - 0002 - 7558 - 6232
Lupane State University, Zimbabwe
Rahabhi Mashapure
https:// orcid .org/ 0009 - 0002 - 6526 - 9169
Chinhoyi University of Technology, Zimbabwe
Purity Hamunakwadi
https:// orcid .org/ 0000 - 0003 - 2940 - 4036
Nelson Mandela University, South Africa
ABSTRACT
In today's fast- paced and ever- evolving business landscape, effective risk management
and pricing strategies have become crucial for organisations to stay competitive and
maximise profitability. In order to effectively manage risks, identify possible threats,
DOI: 10.4018/979-8-3693-5912-9.ch010

250
and seize opportunities, businesses need to take a proactive and holistic approach
to risk management. The current business landscape has left businesses of any size
or type, anywhere in the world, facing a wide range of risks which could cause them
long- term harm, from financial penalty to reputational damage. Through a systematic
review of the literature, this book chapter delved into the ways in which technology is
being leveraged to ensure business resilience in risk management, exploring insights
from different perspectives and providing an in- depth understanding of the options
available. This book chapter also proposed strategic approaches to leveraging and
integrating these technologies and innovations into effective risk management and
ensure business continuity and resilience.
INTRODUCTION: BACKGROUND OF THE STUDY
Rapid technological progress has given rise to a plethora of innovative solutions
that are transforming how firms operate and manage risks. Risk management is ‘the
systematic process of identifying, assessing, and mitigating threats or uncertainties
that can affect your organization’ (Gibson, 2023). It comprises assessing the prob-
ability and severity of threats, creating strategies to minimise damage and closely
monitoring how well countermeasures are working. A robust risk management can
assist to lessen the possibility of losses, but inadequate risk management can have
negative consequences on people, businesses and the economy (Kenton, 2023). Risk
management is important since it alerts risk managers of potential threats in their
workplace, allowing them to take proactive steps to reduce the risks that have been
identified (Thomas, 2024). Acquiring insights into the likelihood and severity of
risks assists businesses in distribution of resources more effectively. If businesses
have knowledge of the risks that affect them, they can make decisions about which
risks demand the most attention and resources and which ones they can afford to
ignore. Companies can proactively correct internal weaknesses before they cause
major challenges by implementing risk management strategies.
As a result of the Fourth Industrial Revolution, management of risks is enhanced
through digital technologies. According to Biolcheva et al. (2022), Le Coze and
Antonsen (2023), and Perera et al (2023), in the contemporary business landscape,
risks are often too complicated and unpredictable for traditional risk management
strategies to effectively tackle them. Among the main advantages of leveraging
digital technologies in risk management is the automation of risk assessment pro-
cesses (McGrath, 2023). Digital technology has revolutionized the discipline of risk
management, allowing businesses to enhance collaboration and communication,
automate and streamline their processes and enhance the accuracy and effectiveness
of risk assessments (Thompson, 2023). Modern communication technologies (such

251
cloud- based collaboration platforms, artificial intelligence chatbots, and virtual
reality platforms) enable stakeholders to communicate openly and share informa-
tion, which promotes more informed risk assessment and better decision- making
(Kenton, 2022; Zamiri & Esmaeili, 2024). Through leveraging digital technological
applications and solutions, businesses create a room for effective risk management
practices since they are tackling difficult problems and presenting new opportu-
nities for businesses to improve their resilience and risk management capabilities
(Thompson, 2023). Automated risk assessment algorithms can track news, social
media sentiment, and market patterns to find possible early warning indicators of
market volatility, regulatory changes, or emerging threats (Kalogiannidis et al.,
2024). This assists businesses to predict risks and take proactive steps to lessen
them before they become realities. Additionally, automated technologies can make
reporting and compliance easier, ensuring that businesses adhere to regulations and
have a solid risk management strategy (Bevz & Domanska, 2024; Haddad, 2023).
By automating some risk management activities, organizations can decrease the
likelihood of bias and human error while increasing the accuracy and objectivity
of risk assessments.
Businesses can determine their risk exposure in real time and take proactive
steps to lower it through leveraging digital technologies (Božić, 2023b). With the
use of risk management software and dashboards, businesses could track key risk
indicators, track risk metrics, and provide comprehensive reports on risk profiles
(Guevara, 2023). For instance, real- time monitoring of credit scores, market pric-
es, and economic indicators can reveal important information about how outside
factors may impact investment portfolios (Varadarajan et al., 2015). This makes it
possible for businesses to maximize risk- adjusted profits and quickly modify their
risk management approaches. Technology makes complicated scenario analysis and
stress testing possible, allowing businesses to evaluate how resilient their invest-
ment portfolios are to various risk scenarios. Through the incorporation of several
hypothetical market situations and stress variables into risk management models,
entities can assess the potential consequences on their investment holdings (Haddad,
2023). This aids in locating weak points and creating robust risk- reduction strate-
gies. Through enabling proactive risk mitigation and improving decision- making,
technology integration into risk management procedures eventually helps to the
long- term success of enterprises. Organizations can improve their decision- making
procedures, efficiency, and stakeholder participation and communication by utilizing
technology in risk management (Bevz & Domanska, 2024). Businesses can improve
their risk management skills and more adeptly traverse the ever- changing business
landscape by leveraging the potential of emerging technology.

252
As artificial intelligence and technology are incorporated into established risk
management procedures, risk management will look different in the future (OECD,
2023). By using advanced analytics, machine learning, and predictive modeling,
organizations can obtain a deeper understanding of risks, automate procedures, and
maximize risk mitigation strategies (Rodríguez- Espíndola et al., 2022a). In order to
thrive in a corporate climate that is continuously changing, firms must now embrace
digital technology in risk management. The importance of digital technology in risk
management will increase as more and more firms embrace digital transformation
and data- centric decision- making. Organizations may enhance their risk manage-
ment capabilities, promote sustainable growth, and gain a competitive advantage
in a dynamic business landscape by harnessing the potential of digital technology.
Given the above background, the current study investigated the impact of digital
technologies in enhancing business resilience in risk management. A literature review
was adopted to identify existing research on digit al technologies in r isk management.
This study advances our understanding of the role of digital technologies in busi-
ness resilience. Its findings inform strategies for effective digital risk management,
ultimately enhancing organizational sustainability. The study provides insights into
the effectiveness of digital technologies in enhancing business resilience. It identi-
fies key challenges hindering the adoption of digital solutions in risk management.
This research offers actionable recommendations for practitioners, policymakers
and risk managers, enabling effective integration of digital technologies into risk
management strategies.
Objectives
The objectives of this manuscript are to:
1. To identify and analyse the impact of digital technologies in enhancing business
resilience in risk management
2. To explore the challenges of leveraging digital technologies in risk management
Methodology
The systematic review of the literature aims to provide a comprehensive and struc-
tured analysis of existing literature on the role of digital technologies in enhancing
business resilience in risk management. To achieve this, a rigorous methodology
was employed to identify, select, and synthesize relevant studies. The search strategy
involved a combination of electronic database searches, hand- searching of reference
lists, and citation tracking. The following terms and phrases were used for searching
research articles from databases such as Scopus, Web of Science and Google Scholar

253
(“digital technologies” OR “Artificial Intelligence” OR “Machine Learning” OR
“Internet of Things” OR “Blockchain”) AND (“risk management” OR “business
resilience”) AND (“organization” OR “industry” OR “sector”). This search strategy
ensured a comprehensive coverage of relevant studies and this led to the identifi-
cation of 65 articles used in this study. Study selection was based on predefined
inclusion and exclusion criteria. Peer- reviewed articles published in English between
2018 and 2024 were included, while studies focusing solely on non- peer- reviewed
publications were excluded. Titles and abstracts were used for the first screening,
and the whole text was then reviewed for eligibility and quality evaluation. Data
extraction involved collecting study characteristics, digital technologies examined,
risk management and business resilience contexts and key findings and conclusions.
Data synthesis involved a narrative synthesis of findings, organized by digital tech-
nology and risk management context, and thematic analysis of key findings and
conclusions. Results are presented in a clear and concise manner, highlighting study
characteristics, digital technologies, and risk management contexts, as well as key
findings and conclusions. The discussion section interprets the results, highlighting
what other scholars have said and this gives room for identification of research gaps
and future research directions. Finally, the conclusion summarizes the systematic
review's objectives and findings, emphasizing the significance of digital technolo-
gies in enhancing business resilience in risk management. This systematic review
employs a precise methodology to provide a comprehensive analysis of existing
literature on digital technologies in business resilience and risk management. The
findings offer valuable insights for practitioners and researchers, highlighting the
importance of digital technologies in enhancing business resilience and informing
future research directions.
LITERATURE REVIEW
Digital Technologies and Risk Management
According to the findings of an OECD study (2023), insurance companies world-
wide are progressively leveraging external data sources to enhance their “traditional”
data and incorporating artificial intelligence and machine learning- based analytical
tools into their risk assessment, underwriting, and pricing determinations. However,
the OECD (2023) study also reveal that insurance companies have a lot of obstacles
when it comes to taking advantage of the potential advantages of new data sources,
analytical tools, and systems for policyholder engagement in risk assessment and r isk
reduction support. The lack of consumer willingness to share data with insurance
companies is one of the key reasons why it is difficult to obtain the expertise, data,

254
and technology needed to combine external data and sophisticated analytical tools
in many countries (OECD, 2023). A study carried out by Rodríguez- Espíndola et
al. (2022) additionally came to the conclusion that supply chain resilience can be
enabled by emerging technology. This study indicates that organizational resilience
is only important when blockchain and AI are adopted. According to Rodriguez-
Espíndola et al. (2022), the reason artificial intelligence and blockchain technology
seem to be gaining traction is that they are more extensively promoted and frequently
linked to the concept of “disruptive technologies.”
According to a study carried out Aziz and Dowling (2018) to assess the capacity
of artificial intelligence and machine learning in risk management, results indicates
that these technologies can help orgainisations to be resilient against risks. However,
Aziz and Dowling (2018) pointed out that before AI and machine learning techniques
for risk management can reach their full potential, there are a number of important
practical difficulties that must be resolved. The availability of appropriate data
is the most crucial of these. The speed at which machine learning solutions have
been proposed has not kept up with businesses' abilities to appropriately organize
the internal data they have access to. This is despite the fact that machine learning
packages for Python and R can easily read all types of data from Excel to SQL,
perform natural language processing, and process images (Aziz & Dowling, 2018).
Guerra (2024) revealed by a study in Italy that artificial intelligence (AI) tech-
nologies can enhance risk management practices in healthcare settings, strengthen-
ing patient safety and standards and raising the bar for overall care. By leveraging
AI- driven predictive analytics, natural language processing, and machine learning
algorithms, hospitals may proactively identify and reduce risks, optimize resource
allocation, and enhance clinical outcomes. According to Guerra (2024), artificial
intelligence (AI) has a lot of potential to improve risk management procedures in
hospitals by facilitating the early detection and mitigation of risks to enhance patient
safety and therapeutic results. Hospitals may optimize the allocation of resources,
minimize adverse events, and ultimately improve the quality of care delivery by
utilizing predictive analytics, natural language processing and machine learning.
A study by Singh (2024) identified the role played by machine learning (ML)
technologies in the financial sector to enhance resilience in credit risk manage-
ment. According to a study by Singh (2024), the findings underscore the critical
importance of integrating ML technologies into banking and finance operations
to bolster risk mitigation strategies and effectively combat financial frauds. By
harnessing the capabilities of ML, financial institutions can significantly enhance
their ability to identify and address potential risks, thereby fostering a more secure
and resilient financial ecosystem (Singh, 2024). Similar results also came from a
study carried out by Rakesh et al. (2024) who recognized how machine learning
may revolutionize the way fraud is detected in financial transactions and credit risk

255
evaluations. Financial institutions may improve their decision- making practices,
reduce risks, and prevent fraudulent activity by utilizing sophisticated algorithms
and data- driven strategies. This, in turn, can help to create a more robust and safe
financial ecosystem (Rakesh et al., 2024).
A study conducted in Nigeria by Nwekwo et al. (2024) shows how data analytics
is improving risk management procedures in the accounting field by facilitating real-
time risk identification, improved predictive modeling, and strategic decision support.
Data analytics has been shown to increase accuracy, automate repetitive activities,
and enable dynamic risk monitoring in risk management procedures. Nwekwo et al.
(2024) recognized problems pertaining to the ethical use of data in risk management,
technological infrastructure, and data quality. Similar results also came from a study
carried out by Obaid (2023) who noted that enhanced adaptability, stronger risk
management, and better project performance are all fostered by big data analytics. In
another study carried out by Khatib et al (2023) findings show that risk assessment
has been transformed by the use of big data in numerous businesses worldwide.
Organizations may now gather, store, and evaluate vast amounts of information to
help with risk assessment and management thanks to big data technology. With the
advent of big data analytical technologies, the procedures for risk identification,
evaluation, mitigation, monitoring, and reporting have been completely redesigned.
Booth (2023) noted that businesses can gain insights, streamline operations, improve
customer experiences, and reduce risks by utilizing the enormous volumes of data
at their disposal and a variety of analytics tools. Studies by Wilhelmina et al (2024)
further demonstrate how predictive analysis contributes to resilience by showcasing
how it may improve risk assessment accuracy, decision- making flexibility, and overall
banking performance. According to Wilhelmina et al (2024), predictive analytics
offers more accurate risk assessments, wiser choices, and more resilience, making
it an essential tool for reducing credit risk in the banking industry.
According to a study carried out by Kalogiannidis et al (2024) in Greece among
IT professionals, the results demonstrate the significant influence of AI technologies
on predictive risk assessment and business continuity. Notably, it was found that
risk assessment processes were faster and more accurate when using natural lan-
guage processing. Particularly successful was the use of AI into incident response
strategies, which significantly reduced business disruptions and enhanced recovery
from unanticipated occurrences (Kalogiannidis et al., 2024). According to Liebergen
(2017), machine learning techniques' capacity to analyze massive volumes of data
while providing a high degree of granularity and depth in predictive analysis can
greatly enhance analytical skills in a variety of risk management and compliance
domains, including credit risk modeling and money laundering detection. Similar
results also came from a research carried out by Cerrone (2023) who observed
through a research carried out in Italy that artificial intelligence and machine learning

256
are enhancing the monitoring of risks and the controls of frauds. According to a
study conducted by Tak (2023), machine learning models for risk management are
outperforming conventional techniques. Similar results to those of a study by Tak
(2023) came from a study by Moradi and Rafiei (2019) who conducted an analysis
using data mining techniques to estimate credit risks in the Iranian financial system;
the study's findings suggest that credit risk prediction using data mining techniques
appears to be more accurate than standard models.
A study carried out by Wong et al. (2022) in Malaysia in order to examine the
role played by artificial intelligence enhanced risk management in enhancing supply
chain reveal that the use of AI for risk management influences supply chain re-
engineering capabilities and agility. Re- engineering capabilities further affect and
mediate agility. Furthermore, Wong et al. (2022) posited that with AI, it is possible
to model various scenarios to answer crucial questions that archaic infrastructures
are not able to. In a study carried out by Hajj (2023) among financial professions
from USA and Europe, the results reveal a growing incorporation of AI and ML
technologies within financial institutions, with a significant proportion of surveyed
participants disclosing moderate to significant usage within their establishments.
According to the research findings from the study by Hajj (2023), algorithmic trading,
risk management, fraud detection, credit scoring, and customer service emerged as
the predominant applications of AI and ML.
According to a research carried out by Nie and Nie (2023), a new financial appli-
cation mode for financial investment is created by the use of big data analysis, which
offers a more sophisticated and digital risk analysis system for managing financial
investment risk. Big data analysis technology offers dependable data resources for
financial investments made by businesses. Simultaneously, it can forecast and man-
age the whole financial risks of the business, significantly enhancing the anti- risk
capacity and economic advantages of businesses (Nie & Nie, 2023). Utilizing big
data analysis technologies can help businesses reduce financial losses, effectively
identify financial risks, and change their investment plans in addition to promoting
enterprise financial investment risk management.
Despite the growing body of research on digital technologies in risk management,
several gaps exist. The rapid evolution of emerging technologies has transformed the
risk management landscape. However, research has struggled to keep pace with the
development and application of innovative solutions. Research has barely scratched
the surface of AI- driven risk assessment and prediction models. Existing studies
primarily focus on traditional statistical methods, neglecting AI's capabilities. By
exploring these uncharted territories, this study can unlock a deep understanding
of the role played by digital technologies in transforming risk management into a
more efficient, effective and proactive discipline

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Challenges of Leveraging Digital
Technologies in Risk Management
According to a research carried out by Boukherouaa et al (2021), digital tech-
nologies have brought in new challenges despite their capacity to enhance business
resilience in risk management. Boukherouaa et al (2021) suggested that these systems
also create new, distinct threats to the safety and integrity of the financial system, as
well as ethical issues, the full scope of which has not yet been determined. The fact
that these innovations are still developing and changing as new technologies become
available makes the work facing policymakers in the financial industry even more
difficult (Boukherouaa et al., 2021). These developments call for the improvement
of oversight monitoring mechanisms and active stakeholder involvement in order to
detect potential hazards and undertake corrective regulatory actions. Another study
Vierescu and Toader (2023) in Romania also indicated that adoption of digital tech-
nologies is bringing in new unique risk like cyber risks. In recent years, cyber risks
have grown in importance for the banking industry. The quantity of sensitive data
being stored and sent online has expanded along with the digitization of banking
services, making banks a target for hackers (Mutanda & Chrispen, 2023).
Studies by Velev et al (2023) show the various obstacles that come with using
digital technologies. Velev et al. (2023) recognized several problems, such as the
requirement for diversified and high- quality data, technology compatibility, ethical
and social ramifications, and compatibility with current systems and technologies.
Risk management frequently entails gathering and analyzing sensitive data. For AI
to be developed and applied in ways that are fair and successful in lessening the
effects of disasters, these issues must be resolved. To preserve customer privacy
and maintain financial laws, the use of AI and ML technologies in fraud detection
must be coordinated with stringent privacy standards.
In another study by Scarpino (2022), participants in the study emphasized the
need for organizations to fundamentally educate all individuals involved in these
technologies, from concept to deployment through decommissioning, to understand
the associated risks and ethical issues present. They also emphasized the risk that
comes with adopting AI and ML technologies and the associated impacts that these
technologies can have on individuals and society (Scar pino, 2022). In a study carried
out by Ahmed and Khalid (2024), the need to incorporate upskilling initiatives,
public awareness, partnerships, and regulatory assistance from government was
emphasized. The results suggest that problems exist surrounding data, infrastructure,
skills, and cultural perspectives of AI. In another study carried by Masakona (2019),
findings show that the banking industry's embrace of rapidly evolving technologies
has resulted in the rise of difficult- to- identify, quantify and manage risks. One of
the major concerns regarding AI implementation, according to a survey conducted

258
among the top 1,000 US enterprises, was personnel preparation and ability to engage
with these new digital solutions (Wilson et al, 2017).
Organisational and Cultural Changes Required to
Implement Digital Technologies in Risk Management
According to Kivanc (2023), long- term success for businesses will depend on
their capacity to embrace adaptation, constant learning, and a people- centric strategy
as they navigate this quickly changing world. According to Kivanc (2023), leaders
are essential in creating an environment that values ethics above all else and wel-
comes innovation while maintaining a careful balance between utilizing emerging
technologies and respecting fundamental human values. By doing this, businesses
can build an atmosphere that is inclusive, encouraging and resilient for all parties
involved. Studies by Božić (2023a) highlighted the need for training of employees
as a way of helping them adapt to new processes and technologies. The results of
another study by Bley et al. (2022) show a significant positive correlation between
AI capabilities, organizational performance, and organizational culture. According
to the National Institute of Standards and Technology (2023), changes in corporate
culture may be necessary for effective risk management, which is achieved through
senior leadership commitment. In another study by Drmac (2022), the results show
that organizations must educate individuals when implementing digital technology
like artificial intelligence. This entails filling up knowledge gaps that conflict with
AI specifications. As the implementation crosses over into other business units,
it is imperative to establish roles and delegate AI- related activities to pertinent
stakeholders. Teams and users should receive technical assistance and AI training
during the deployment process (Drmac, 2022). Participants emphasized the signif-
icance of this approach by pointing out that it facilitated enhanced internal skills
and decision- making for the primary business processes, which was challenging
because of capacity unpredictability.
According to a study by carried out by Wang (2023) in China, the results indicate
certain factors that small and medium- sized enterprises need to take into account in
order to adopt digital technology successfully. Management strategies like enhanc-
ing organizational learning, technological and robust capabilities, implementing
cybersecurity governance and initiating educational training courses are advised
in order for SMEs to successfully implement digital technologies for risk manage-
ment. According to a study by Mart et al. (2023), MSMEs in Mexico are unable to
successfully use technology due to a lack of digital skills and expertise. In light of
this, proprietors or senior managers need to acknowledge the value of digital literacy
and guarantee that staff members have access to resources and training programs
that will improve their digital proficiency. According to Mart et al (2023), MSMEs

259
can sustainably give their staff the skills they need to adopt and use digital technol-
ogies for operational efficiency and growth propelled by their digital capabilities by
cultivating a learning culture and offering the required assistance. Similar finding
came from a study by Rodríguez- Espíndola et al (2022b) and Chowdhury et al
(2023) who discovered that the intention to use blockchain technologies for risk
management in the UK business sector was significantly and favorably influenced
by knowledge of the advantages of blockchain technologies, participation in robust
organizational practices, and user- friendly technology implementation. Studies by
Syafruddin and Kurnia (2023) in Indonesia outlined some factors that should be
taken into account when implementing digital technology for risk management.
According to Syafruddin and Kurnia's (2023) findings, exploring automation as
a way to optimize procedures can improve operational efficacy. Digitalizing risk
management, according to Syafruddin and Kurnia (2023), lessens SMEs' reliance
on human labor and enables them to better manage their resources and adapt to
changing market conditions.
RESULTS AND DISCUSSION
Impact of Digital Technologies in Risk Management
According to a study by Aziz (2018), machine learning and artificial intelligence
are widely used in risk management. AI is progressively providing precise real- time
information on all kinds of risks that the company is taking (Aziz, 2018). Real- time
guidance will become more and more prevalent as data organizations focus more
on using AI. Similar observations were made by Denning (2023) who argued that
artificial intelligence (AI) plays a crucial role in data analysis and insight generation,
giving organizations useful insights and predictions to guide decision- making. In the
banking industry, Abbas (2024) claimed that machine learning models might save
costs and improve scalability by automating repetitive procedures, optimizing loan
decisions, and streamlining back- office operations. According to Denning (2023),
artificial intelligence and machine learning have the potential to enhance human
abilities and elevate the standard of output, freeing up workers to concentrate on
more important duties that call for creativity and analytical thinking. According to
Brintrup et al (2023) and Raza (2024), using AI- powered predictive maintenance
improves operational continuity significantly. By analyzing sensor data and equip-
ment performance indicators, artificial intelligence (AI) in the manufacturing sector
may predict when there is a chance that machinery and infrastructure will break
down or when maintenance is needed and this helps management make decisions.
The implementation of preventive measures is crucial for maintaining business

260
continuity, especially in the industrial and critical infrastructure sectors (Fan et al.
2019; Ray, 2023).
In agreement to results of a study by Aziz (2018), Denning (2023), Brintrup et
al (2023) and Raza (2024), Ibitola (2023) observed that with its unmatched data
processing power and prediction ability, artificial intelligence (AI) has completely
changed the way we perceive, assess, and manage financial risks. Financial insti-
tutions are using artificial intelligence (AI) to predict future market behaviours,
extract insights from large databases, and make better judgments than ever before, all
without depending on human intuition or historical trends. Financial organizations
are using more proactive strategies to identify and neutralize cyber threats before
they materialize. Traditional models based on historical data are being replaced by
predictive analytics to identify and adjust to future threats (Aldoseri et al., 2023;
Gaewprapun, 2024). These arguments align with the conclusions drawn by Gunning
(2017) who demonstrated the effectiveness of predictive analytics in identifying and
mitigating potential risks. The sheer volume of transactions occurring every second
renders traditional methods of risk assessment impractical. Financial institutions
need systems that can quickly and accurately make judgments by sifting through
massive amounts of data in real- time, identifying anomalies. These demands are
not only met, but frequently exceeded by digital technology, which offer a level of
insight and foresight that was previously unfathomable.
As argued by Aziz (2018), Olushola and Mart (2022) revealed that machine
learning algorithms could identify anomalies in real- time, minimizing false posi-
tives and quickly identifying possible risks, because they were trained on enormous
datasets of real and fraudulent transactions. The same observations were made by
Sandepudi (2024), who showed that banks may examine large datasets to effectively
identify and prevent fraud by employing machine learning, moving from reactive to
proactive approaches. This calculated move shows the banking industry's dedication
to innovation and data security while safeguarding consumers and maintaining the
integrity of financial transactions. According to Sabu (2024), fraud detection was
previously mostly accomplished with conventional rule- based systems. Due to their
reliance on preset criteria and procedures, these systems frequently found it difficult to
adjust to the changing landscape of illicit activities. These rule- based systems became
more and more ineffective as fraudsters changed, which increased the percentage
of false positives and missed fraudulent transactions (Sabu, 2024). The results of a
study by Sabu (2024) reveal that artificial intelligence (AI) and machine learning
(ML) effectively identify suspicious patterns, including circular money movements
and transactions switching between multiple accounts. This finding aligns with the
research conducted by Sandepudi (2024), who similarly argued that AI and ML excel
at detecting complex patterns. This facilitates the identification of possible money
laundering and the tracking down of payments to their illicit origin. The secret is to

261
use sophisticated network graphs to highlight intricate relationships that would be
missed by manual or insufficient transaction tracking. When it comes to identifying
loan fraud (fake information supplied for loan acquisition) and credit card fraud
(unauthorized use of stolen card information), machine learning algorithms are at
the forefront (Olaoye & Blessing, 2024).
In contrast to the observations made by Sandepudi (2024), Srivastava et al.
(2018) found that the primary challenge of using AI and machine learning in the
prediction of financial and cyber risk is the availability and quality of data. AI and
ML algorithms require vast amounts of relevant and accurate data to learn and im-
prove. However, financial institutions often struggle with data silos, inconsistent
formatting, and inadequate data governance (Bolton et al., 2019). Additionally,
Gunning’s (2017) findings contradict results of a study by Sabu (2024) as they
observed that many businesses lack of skills for interpreting and explaining model.
AI and ML models can be complex and opaque, making it difficult to understand
the reasoning behind their predictions (Gunning, 2017). This lack of transparency
hinders the ability to identify false positives and negatives, potentially leading to
incorrect risk assessments.
Challenges of Leveraging Digital
Technology in Risk Management
There is an agreement among scholars (Civilcharran & Maharaj, 2023; Asian
Development Bank, 2023; Wang, 2023; Minor et al., 2024) that the use of digital
technologies in risk management necessitates the hiring of workers with the necessary
skills who are knowledgeable about cutting- edge fields like cybersecurity, cloud
computing, artificial intelligence, and data analytics. But a lot of businesses have
trouble finding and keeping individuals with the right skill sets. Despite the great
contributions made by digital technology in risk management, Masakona (2019),
found that a lot of businesses have trouble finding and keeping individuals with
the right skill sets. Similar observations were also made by Shread et al. (2018),
who noted that the shortage of skilled professionals in risk management and digital
technology hinders organizations' ability to implement and maintain effective risk
management systems. In agreement to the arguments raised by Masakona (2019),
Deloitte (2020) reported a staggering 62% of organizations struggle to recruit and
retain cybersecurity professionals with expertise in emerging technologies, such as
artificial intelligence and machine learning. Moreover, businesses can draw in and
keep top digital talent by developing an appealing workplace culture that values
creativity and offers chances for career advancement. Similar observations were

262
also made by Chowdhury et al (2023) who asserted that the availability of workers
with digital skills determines the efficacy of digital technology in risk management.
Enterprises face enormous hurdles in handling, storing, and safeguarding the
massive amounts of data generated by digital technologies (Xu & Zhang, 2023).
According to Quach et al (2022), it is essential to guarantee data privacy, regulato-
ry compliance, and defense against cybersecurity threats. The same argument was
also raised by Bansod and Ragha, (2022) who emphasized that in order to protect
sensitive data, businesses must invest in reliable data management systems, put in
place suitable data governance procedures, and use encryption and authentication
techniques. Businesses should also set up procedures for data access and usage and
teach their staff about data privacy. Haugum et al (2022) claimed that, in spite of
the advantages of digital technologies for risk management and business resiliency,
blockchian technologies are tethered to many security and privacy issues, including
Notary Schemes, Sidechains, and Hashed Time- Lock Contracts. According to Man-
ish and Dave (2024), data integration ‘refers to the process of combining data from
different sources to provide a unified view’. Interoperability, on the other hand, ‘is
the ability of different digital technologies and devices to work together, regardless
of the manufacturer, technology, or protocol’ (Manish & Dave, 2024). Businesses
frequently use a wide range of incompatible, non- integrated systems and apps that
are not integrated or interoperable. Interoperability and data integration present a
variety of challenges. It covers the technological challenges brought on by the di-
versity of systems and devices as well as the requirement for common standards and
protocols to facilitate smooth communication. Duarte- vidal et al (2021) suggested
that when digital technologies are adopted for risk management, a major difficulty
becomes ensuring a smooth flow of data and operations across multiple platforms.
To tackle this, businesses should give priority to interoperability standards, imple-
ment integration platforms, and use application programming interfaces (APIs) to
facilitate data exchange and cooperation amongst various stakeholders and systems
(Vu & Pham, 2024). This integration promotes innovation and increases efficiency
by creating a unified digital ecosystem.
The digital infrastructure of businesses presents another difficulty that several
academics have noted when adopting digital technology to improve business resil-
ience in risk management. According to Smidt and Jokonya (2022), any businesses
still use antiquated infrastructure and legacy systems that are incompatible with
digital technologies. A new technology's integration with an existing system can
be costly, time- consuming, and complex. Businesses must evaluate their current
systems, pinpoint opportunities for development, and create a phased moder nization
strategy in order to meet this challenge (Avenyo et al., 2022). This could include
replacing monolithic legacy systems with modular ones, implementing application
programming interfaces (APIs) for improved data integration, and moving to cloud-

263
based platforms. Similar to the arguments raised by Smidt and Jokonya (2022) that
businesses are currently facing challenges of digital infrastructure, authors such as
Bolton et al. (2019) and Srivastava et al. (2018) also highlight the significance of
digital infrastructure in AI and ML adoption. They argue that inadequate digital
infrastructure hinders the effective implementation of AI and ML models, leading
to suboptimal risk management
Despite the role played by digital technologies in enhancing business resilience
in risk management, scholars such as Mutanda and Crispen (2023), Vierescu and
Toader (2023), Nguyen Duc and Chirumamilla (2019) and Kessler et al (2022)
asserts that businesses are exposed to new risks and vulnerabilities as a result of
digital technologies. There may be serious repercussions from cybersecurity risks,
data breaches, and problems with regulatory compliance. Businesses must put
strong cybersecurity safeguards in place, set up proactive monitoring and incident
response procedures, and regularly review risks in order to reduce potential dan-
gers. Risk management procedures must be integrated into a digital transformation
strategy from the beginning in order to safeguard the company, its resources, and
its stakeholders. (Asian Development Bank, 2023).
Further Research
The literature review on leveraging digital technologies to enhance business resil-
ience in risk management has provided valuable insights into the potential benefits
and challenges of integrating digital solutions into risk management frameworks.
However, despite the progress made, there remains a significant need for further
research and exploration. One potential area for future research is the development
of a comprehensive framework for harnessing digital technologies in risk man-
agement in business. Existing frameworks and models provide a foundation, but
a comprehensive framework would facilitate better understanding and application
of digital technologies in building resilient businesses. Additionally, researchers
could investigate the relationship between digital resilience and organizational
performance, exploring how digital technologies impact business outcomes. The
intersection of digital resilience and cybersecurity is another critical area for in-
vestigation. As businesses increasingly rely on digital technologies, cybersecurity
threats pose significant risks to resilience. Researchers could explore strategies
for integrating cybersecurity into digital risk management frameworks, enhancing
overall business resilience. Furthermore, the impact of digital resilience on supply
chain management warrants attention. With global supply chains facing increasing
complexities and disruptions, digital resilience strategies can mitigate risks and
improve adaptability. It is critical for researches to be carried out and discover how
digital technologies can be used for risk management in such fields as healthcare and

264
manufacturing. Lastly, the other area of interest still lacking in terms of researches
are guidelines for ensuring digital resilience in the face of emerging risks such as
quantum computing threats.
Managerial Implications
Given the contributions made by digital technologies in ensuring business resilience
in risk management, managers should prioritize investing in digital technologies.
The days of relying just on conventional risk management techniques are gradually
ending, especially in light of the vast amounts of data that businesses handle. It
is the responsibility of management to allocate funds, purchase these technology,
and guarantee risk management resilience. The organization's ability to analyze,
detect, and mitigate risk will be enhanced by investments in digital technologies like
machine learning, artificial intelligence, data analytics, and the internet of things.
It is also very imperative for business managers to come up with a digital
transformation strategy which align with the organisation’s overall goals and risk
management objectives. The digital transformation strategy is meant to clarify the
digital roadmap that the organisation will have to consider in the short term, medi-
um term or the long term. Managers also need to devise ways of building a culture
of innovation and experimentation in the organisation as a strategy for dealing
with resistance to change among the employees. Given the challenges digital skills
shortages highlighted in this chapter, it is also very critical for business managers
to consider training and upskilling employees so as to close digital skills gaps and
prepare enterprises for digital technologies adoption. The results presented in this
manuscript call for managers to adopt a data- driven decision making. When making
decisions about risk management, managers should rely on data analytics and insights
from digital technology. In order to reap the benefits of digital technologies, manag-
ers must encourage cooperation between departments and stakeholders in order to
optimize the use of digital technologies for risk management. Since it has already
been said that cyber dangers are closely related to digital technology, managers must
give cyber security measures top priority in order to safeguard sensitive company
and consumer data. Employing digital technologies raises ethical questions that
managers have to deal with about algorithmic bias, data privacy, and transparency.
In the absence of cyber risk mitigation, the benefits of utilizing digital technology
would not be realised. Establishing strategic connections with digital technology
providers and specialists is crucial for managers to maintain a competitive edge in
risk management.

265
CONCLUSION
This research has demonstrated that leveraging digital technologies is crucial
for ensuring business resilience in risk management. The effective integration of
digital technologies, such as artificial intelligence, blockchain, data analytics, cloud
computing and the Internet of Things (IoT), can enhance risk detection, assessment,
and mitigation capabilities. Digital technologies can also facilitate real- time moni-
toring, improve collaboration and communication, and enable data- driven decision-
making. The findings of this research highlight the importance of embracing digital
transformation in risk management, and provide insights for organizations seeking to
leverage digital technologies to strengt hen their resilience. Specifically, this research
recommends that organizations:
1. Develop a digital transformation strategy aligned with their risk management
objectives.
2. Invest in emerging digital technologies to enhance risk management capabilities.
3. Foster a culture of innovation and experimentation to explore new digital
solutions.
4. Ensure effective integration of digital technologies with existing risk manage-
ment systems.
5. Prioritize cybersecurity and data privacy to mitigate potential risks.
By leveraging digital technologies, organizations can enhance their ability to
anticipate, respond to, and recover from disruptions, ultimately ensuring business
resilience in an increasingly complex and uncertain risk landscape.” This conclusion
summarizes the key findings, reiterates the importance of digital technologies in
risk management, and provides actionable recommendations for organizations to
leverage digital technologies and strengthen their resilience.

266
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