The American Journal of Engineering and Technology
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Original Research
PAGE NO.
54-61
10.37547/tajet/Volume07Issue07-06
OPEN ACCESS
SUBMITED
14 June 2025
ACCEPTED
26 June 2025
PUBLISHED
07 July 2025
VOLUME
Vol.07 Issue 07 2025
CITATION
Hari Dasari. (2025). Resilience Engineering in Financial Systems: Strategies
for Ensuring Uptime During Volatility. The American Journal of Engineering
and Technology, 7(07), 54
–
61.
https://doi.org/10.37547/tajet/Volume07Issue07-06
COPYRIGHT
© 2025 Original content from this work may be used under the terms
of the creative commons attributes 4.0 License.
Resilience Engineering in
Financial Systems:
Strategies for Ensuring
Uptime During Volatility
Hari Dasari
Expert Infrastructure Engineer Leading Financial Tech Company Aldie,
Virginia
Abstract:
Financial institutions suffer volatility,
regulatory scrutiny, cyber risks, and complex technical
linkages. System outages and operational failures can
influence market stability, customer trust, and
regulatory compliance in this setting. For proactive
financial system design that can predict, withstand, and
recover from interruptions with little service
deterioration, resilience engineering is essential.
This article examines financial system resilience
engineering strategies in detail. It covers redundancy,
observability, adaptive capacity, microservices, multi-
region deployments, service meshes, Site Reliability
Engineering (SRE), chaotic testing, and real-time
monitoring. It also examines worldwide regulatory
frameworks like the UK FCA recommendations, EU
DORA regulation, and US FFIEC standards, highlighting
regulatory alignment in operational resilience.
JPMorgan Chase's resilience architecture is examined in
detail, along with AI-driven observability, Zero Trust
architectures, edge computing, and blockchain-based
settlements. This research integrates technical,
operational, and compliance methods to help financial
institutions maintain uptime and service continuity in a
dynamic digital economy.
Keywords:
Resilience Engineering, Financial Systems,
Site Reliability Engineering (SRE), Fault Tolerance, Chaos
Engineering, Operational Resilience, High Availability,
Disaster
Recovery,
Market
Volatility,
Incident
Management
1.
Introduction:
Financial institutions face mounting
pressure to provide continuous services across
intricate and decentralized digital networks. Market
volatility, induced by geopolitical tensions,
cyberattacks, or regulatory changes, can result in
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cascade failures. Conventional disaster recovery
methods are inadequate for the current demands of
scale and urgency. Resilience engineering, a
proactive and design-focused profession, is
becoming a fundamental method for ensuring
operational continuity (Hollnagel, 2011). This study
examines the integration of resilience engineering
principles with Site Reliability Engineering (SRE)
methodologies to enhance the capacity of financial
systems to withstand, recuperate from, and adjust
to unfavorable conditions while sustaining essential
operations.
Resilience engineering improves technical robustness
while incorporating cultural, procedural, and regulatory
elements to promote system-wide durability. This article
examines each pillar of resilience and provides visual
graphics and real-world applications for practical
comprehension.
2.
Principles of Resilience Engineering
Resilience engineering is built on four foundational
capabilities: the ability to anticipate, Resilience
engineering, as defined by Hollnagel (2011), is based on
the principle that complex socio-technical systems
should be designed not only to prevent failure but also
to maintain functionality under stress. The fundamental
principles encompass the capacities to foresee, observe,
react to, and derive insights from disturbances. These
qualities constitute the foundation of resilience in
dynamic and high-risk situations, such as financial
systems.
•
Redundancy and Diversity
: These entail the
replication of essential components or functions
inside a system utilizing diverse technologies or
methodologies to avert concurrent failure. Diversity
among cloud providers and geographic locations is
particularly crucial in cloud infrastructure, as it
markedly diminishes the probability of linked
failures (Woods, 2006).
•
Observability
: Basiri et al. (2016) assert that
observability is crucial for sustaining situational
awareness. It involves the application of tools and
methodologies
—
such
as
distributed
tracing,
metrics, and structured logging
—
that enable
engineers to comprehend internal conditions
through outward outputs. This enhances the speed
of failure identification, diagnosis, and resolution.
•
Elasticity and Scalability
: Systems must be designed
to accommodate fluctuating load levels effectively.
According to Burns et al. (2016), cloud-native
architectures facilitate elasticity via container
orchestration and autoscaling techniques, ensuring
performance is sustained during demand surges
without manual intervention.
•
Blameless Culture
: Prioritizing a culture of learning
and transparency, instead of attributing blame,
promotes open communication and systemic
enhancements following incidents. This cultural
value, advocated by the SRE community (Beyer et
al., 2016), guarantees ongoing enhancement via
systematic post-incident evaluation.
These ideas are progressively integrated into
contemporary operational resilience frameworks
required by regulatory authorities like the FCA and
European Commission, emphasizing the necessity
for comprehensive, engineering-centric strategies
for financial stability.
3.
Causes and Impact of Volatility in Financial
Systems
A complex mix of internal and external factors drives
financial system volatility. These systems are vulnerable
to disturbances due to their interconnected
infrastructures and worldwide markets (Kapadia et al.,
2020). Engineering robust and adaptive financial
infrastructure requires understanding volatility's causes
and effects.
•
Market Triggers
: Asset prices and trading volumes
can alter dramatically due to macroeconomic
variables including interest rates, foreign exchange
volatility,
and
liquidity
shortages.
Order
management systems (OMS) and core financial
infrastructure are overwhelmed by simultaneous
transaction loads during these situations. According
to the Bank of England, intraday liquidity volatility
raises systemic concerns if not controlled within
resilient operational constraints (Kapadia et al.,
2020).
•
Cybersecurity Incidents
: Cyberattacks target the
banking sector due to its high-value assets and data.
Ransomware, phishing, and DDoS can cripple
operations. Open banking frameworks and API-
driven third-party integration increase attack
surface (FCA, 2021). Regular threat-led penetration
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testing helps identify vulnerabilities under the EU's
DORA plan (European Commission, 2022).
•
Software Bugs and Releases
: Agile methods and
short release cycles improve feature delivery, but
without adequate testing frameworks, they also
bring hazards. Some recent outages have been
caused by software issues such misconfigured
infrastructure as code (IaC) and improper
deployment scripts (Beyer et al., 2016). Resilience
requires CI/CD pipelines and automatic rollbacks.
•
Third-Party
Failures
:
Financial
institutions
increasingly use cloud services, SaaS suppliers, and
fintech APIs for customer-facing functionality. This
promotes innovation but increases dependence
failures. If not isolated, third-party service provider
failures can affect core systems. The FFIEC
emphasizes
comprehensive
third-party
risk
management (2021).
Cumulative disruption effects include:
•
Prolonged service outages or data breaches can
damage client trust and lead to customer attrition.
•
Financial Losses: Trading downtime or missed
transactions can cost millions in income.
•
Regulatory Penalties: SLA and resilience non-
compliance can result in fines and greater attention.
•
Misperception of instability can damage reputation,
affecting market confidence, share value, and
investor trust.
Quantifying and categorizing these risks helps
institutions build resilience strategies, implement
mitigation measures into design and operations, and
comply with financial stability regulations.
4.
Architectural Strategies for Resilience
Financial system architecture is critical for enabling
resilience. Modern digital infrastructures must be built
with fault tolerance, isolation, and recovery in mind.
Architectural methods provide systemic defensive
mechanisms by preventing failures from cascading and
allowing affected services to recover quickly.
Geographic dispersion is a highly effective resilience
pattern. Multi-region deployments ensure service
continuity during localized outages. Active-active
topologies allow for real-time load sharing and quick
failover, whereas active-passive systems optimize cost
and resilience trade-offs. According to the European
Commission (2022), cross-border plans must consider
data residency restrictions, latency optimization, and
cross-region replication policies.
A microservices design allows for the isolation of failure
areas. Services can be deployed independently, which
reduces the blast radius of failure. For example, a failure
in a fraud detection service has no impact on login or
balance inquiry systems. Kubernetes, Docker, and
orchestration frameworks like OpenShift enable
scalable deployments of loosely coupled services, which
improves agility and fault isolation (Burns et al., 2016).
Event-driven systems, such as Apache Kafka or Amazon
Kinesis, enable asynchronous processing by decoupling
producers and consumers. This approach can handle
load balancing, elasticity, and service recovery during
outages. In financial applications, event-driven patterns
provide dependable order processing, payment
streaming, and real-time fraud detection, all with replay
capability and audit logs (Chen et al., 2021).
Service meshes, like Istio and Linkerd, offer robustness
at the communication layer. They use circuit breakers,
traffic splitting, retries, and rate limitation to prevent
cascade failures. These tools also enable fine-grained
observability
and
secure
service-to-service
communication in zero-trust architectures (Burns et al.
2016).
Immutable infrastructure uses Infrastructure as Code
(IaC) tools such as Terraform and Ansible, in addition to
containerization, to ensure repeatability and rollback
safety. Immutable systems, in which instances are
replaced rather than modified, prevent configuration
drift and make compliance checks easier. As stated in
Capital One's cloud transformation reports, this
technique has resulted in a considerable decrease in
incident frequency and mean time to recovery (Capital
One, 2021).
Real-world examples highlight the effectiveness of these
tactics. Goldman Sachs used microservices and event
streaming to improve operational transparency and
performance throughout its Marcus platform (Goldman
Sachs Engineering, 2020). Similarly, JPMorgan Chase and
Bank of America deploy multi-cloud, active-active
arrangements to reduce the dangers of vendor lock-in
and regional outages.
These architectural strategies work together to offer
smooth deterioration, quick failover, and better
operational transparency. They serve as the foundation
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for high-availability financial systems and are consistent
with resilience concepts defined in the Digital
Operational Resilience Act (DORA) and FFIEC standards.
Financial system architecture is critical for enabling
resilience. Modern digital infrastructures must be built
with fault tolerance, isolation, and recovery in mind.
Architectural methods provide systemic defensive
mechanisms by preventing failures from cascading and
allowing affected services to recover quickly.
Geographic dispersion is a highly effective resilience
pattern. Multi-region deployments ensure service
continuity during localized outages. Active-active
topologies allow for real-time load sharing and quick
failover, whereas active-passive systems optimize cost
and resilience trade-offs. When building cross-border
infrastructures, it is critical to address regulatory
obligations for data sovereignty. Furthermore, data
replication and consistency models must be carefully
constructed to avoid conflicting states and delayed
writes.
A microservices design allows for the isolation of failure
areas. Services can be deployed independently, which
reduces the blast radius of failure. For example,
problems with a payment gateway microservice do not
affect user authentication or balance inquiry services.
Kubernetes and service orchestration tools make this
modular deployment strategy possible (Burns et al.,
2016). This modularization also improves agility by
allowing teams to test and deploy changes without
affecting other components.
Apache Kafka and Amazon Kinesis are examples of
event-driven systems that decouple data producers and
consumers. This architectural model enables more
flexible recovery because messages can be saved and
replayed if services go down. Event sourcing also offers
rigorous auditability, which is a major problem in
financial systems. Asynchronous communication
methods encourage responsiveness and scalability
during peak demand periods.
Istio and other service meshes improve communication
layer resilience by managing traffic flows, enforcing
policies, and facilitating circuit breaking. These tools
provide fine-grained telemetry and dynamic rerouting
to healthy services, minimizing the impact of service
degradation (Burns et al., 2016). Service meshes enable
zero-trust and scalable service discovery by abstracting
security, routing, and observability into a dedicated
infrastructure layer.
Finally,
immutable
infrastructure,
enabled
by
Infrastructure as Code (IaC) and containerization,
provides environmental consistency while simplifying
rollback procedures. Engineers can provision, deploy,
and replace systems in a repeatable and reliable manner
using tools such as Terraform and Docker. Immutable
builds minimize configuration drift, which is frequently
the fundamental cause of production difficulties.
These architectural strategies work together to create a
layered defense that allows for smooth degradation,
rapid recovery, and minimal user impact in the event of
volatility or component failure. These approaches
provide institutions with enhanced agility, regulatory
compliance, and competitive differentiation through
superior customer reliability metrics.
5.
Operational Resilience: SRE and Chaos Engineering
Site Reliability Engineering and Chaos Engineering
Operational resilience denotes an organization's ability
to maintain the provision of essential services in the face
of adverse operational events. In financial systems, this
refers to the capacity to handle demand surges,
effectively address component failures, and recover
from incidents while adhering to Service Level
Agreements (SLAs). Site Reliability Engineering (SRE) and
Chaos Engineering are two disciplines fundamental to
operational resilience.
•
Site Reliability Engineering (SRE):
Originating at
Google and formalized by Beyer et al. (2016), Site
Reliability
Engineering
(SRE)
implements
engineering practices that automate operations,
uphold service reliability objectives, and reconcile
feature development with system stability. SRE
fundamentally depends on Service Level Indicators
(SLIs), Service Level Objectives (SLOs), and Error
Budgets. These metrics inform teams regarding the
acceptable level of risk that can be managed while
ensuring the delivery of reliable services. If a
service's SLO permits 99.9% uptime, the error
budget thus allows for 0.1% downtime during the
measurement period. This budget guides decisions
regarding the implementation of new features in
relation to the prioritization of stability. Financial
institutions such as Morgan Stanley and ING have
implemented SRE frameworks to measure reliability
initiatives across their internal platforms (O'Reilly
Media, 2020).
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•
Chaos Engineering
: A concept developed by Netflix,
involves the systematic introduction of faults into
production-like systems to uncover vulnerabilities
prior to their manifestation as user-visible failures
(Basiri et al., 2016). This practice has become
increasingly prevalent among financial institutions
aiming to assess the resilience of their systems
under real-world stress conditions. Tools such as
Chaos Monkey, Gremlin, and Litmus Chaos enable
teams to replicate node failures, network latency, or
dependency disruptions. JP Morgan and Capital One
implement chaos engineering in both staging and
production environments to validate failover paths
and automated recovery mechanisms (Gremlin,
2021).
•
Monitoring
and
Observability
:
High-quality
telemetry is essential for operational resilience
through real-time monitoring and observability.
Prometheus, Grafana, the ELK stack, and Splunk
offer functionalities such as dashboards, event
logging, and metric-based alerting. Contemporary
observability solutions utilize machine learning to
identify anomalous patterns and forecast incidents
prior to their effect on users. Integration of
runbooks and auto-remediation scripts facilitates
swift resolution. Beyer et al. (2016) identify a
primary objective of SRE as the reduction of Mean
Time to Detect (MTTD) and Mean Time to Repair
(MTTR). In production deployments within financial
institutions, these tools utilize threshold-based and
dynamic anomaly detection models to ensure
compliance with regulatory audit requirements.
•
Operational Procedures and Simulation Exercises
:
Runbooks consist of documented procedures
designed to address known failure scenarios,
whereas Game Days serve as simulation exercises to
practice incident response. Game Days evaluate not
only technical controls but also cross-functional
collaboration,
escalation
pathways,
and
communication
workflows.
They
identify
deficiencies in tools, training, and processes,
thereby enhancing incident preparedness. The SRE
playbook by Google states that Game Days must
replicate both anticipated and chaotic scenarios to
optimize reliability learning (Beyer et al., 2016).
SRE and Chaos Engineering provide a complementary
set of tools for constructing resilient financial platforms.
SRE enforces reliability metrics and engineering rigor,
while Chaos Engineering challenges assumptions by
proactively identifying unknown failure modes. Their
integrated approach results in a cultural transformation,
transitioning from reactive problem-solving to proactive
reliability assurance. Institutions that adopt these
practices frequently experience notable decreases in
service degradation incidents, better adherence to SLAs,
and increased operational transparency.
6.
Regulatory Expectations and Compliance
The stability of financial institutions is receiving more
attention from governments and regulatory bodies
worldwide. Considering the growing vulnerability to
cyberattacks, operational complexity, and reliance on
external parties, these regulations are necessary to
ensure the continuity of essential financial services.
FCA (UK):
Identifying Critical Business Services (IBS),
defining Impact Tolerances, and demonstrating the
ability to stay within those tolerances under a range of
severe but probable scenarios are all requirements of
the Financial Conduct Authority (FCA) in the United
Kingdom (FCA, 2021). Conducting stress tests on
mission-critical services, creating dependency maps
(both internal and external), and establishing
governance frameworks to ensure operational resilience
are all part of this process.
Bank of England:
The Bank of England (BoE) and the
Prudential Regulation Authority have stated that
companies should be able to provide essential services
even when faced with disruptions, rather than just
getting back up and running after a failure (Kapadia et
al., 2020). The focus is on proactively addressing
vulnerabilities through the design of architecture and
procedures.
FFIEC (US):
In the United States, the Federal Financial
Institutions Examination Council (FFIEC) lays forth the
groundwork for managing third-party vendors,
conducting cyber risk assessments, and planning for
business continuity. The necessity of end-to-end
operations-covering resilience plans, risk identification,
and governance is stressed in their IT Examination
Handbook (FFIEC, 2021).
DORA (EU):
According to the European Commission
(2022), financial institutions are obligated to implement
consistent
protocols
for
information
and
communication technology (ICT) risk management,
incident categorization, digital operational testing
(including threat-led penetration testing), and third-
party monitoring. The objective of DORA is to bring the
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European Union's financial sector's cybersecurity and
operational risk supervision in line with one another.
Basel Committee on Banking Supervision (BCBS):
The
Basel Committee on Banking Supervision (BCBS) has
issued
recommendations
regarding
operational
resilience, recommending that international financial
institutions include resilience measures and cross-
functional plans in their risk management plans (BCBS,
2021).
In addition to being required by law, complying with
these requirements can help advance your strategy.
When institutions adjust their procedures to meet
regulatory standards, they gain the trust of their
customers, the confidence of their investors, and the
ability to withstand market-wide upsets. In addition, it
helps close the gap between BCP and real-time
operational capacity by encouraging a resilient culture
that incorporates risk reduction into routine operations.
7.
Case Study: JPMorgan Chase
JPMorgan Chase stands out as a leader in implementing
resilience engineering principles across a globally
distributed infrastructure. With operations spanning
retail, investment banking, and asset management, the
institution faces high transaction volumes and strict
regulatory oversight, necessitating robust resilience
practices.
•
Telemetry and Monitoring
: JPMorgan employs a
centralized observability stack that aggregates logs,
metrics, and traces across services. Real-time
telemetry feeds into anomaly detection algorithms
that support early warning systems and automatic
remediation workflows (Kharif, 2022). These
telemetry platforms integrate with incident
management systems to ensure efficient escalation.
•
Cloud Redundancy and Multi-Cloud Strategy
: To
mitigate vendor lock-in and ensure availability,
JPMorgan uses a hybrid multi-cloud model that
spans both private data centers and public cloud
providers. Their infrastructure supports active-
active
failover
between
regions,
ensuring
compliance with data residency regulations while
minimizing downtime (CNBC, 2022).
•
Automated CI/CD and Rollbacks
: The firm utilizes
advanced deployment pipelines with built-in canary
testing and automated rollback mechanisms.
Changes are continuously tested in lower
environments with fault injection, and promotion to
production is governed by real-time SLO
compliance.
•
Chaos Engineering and Game Days
: JPMorgan
integrates chaos engineering experiments into their
CI/CD lifecycle using tools such as Gremlin and
custom fault injection frameworks. These tests
simulate realistic failure scenarios
—
such as
degraded latency, instance crashes, or loss of
connectivity
—
to validate system behavior under
stress (Gremlin, 2021). In addition to automated
experiments, the firm regularly conducts Game Days
involving cross-functional teams.
•
Regulatory Alignment and Stress Testing
: The firm
aligns with global mandates including the FCA's IBS
identification guidelines, the US FFIEC handbook,
and the EU’s DORA. Their resilience strategy is
subjected to internal and third-party audits, and
JPMorgan actively participates in industry-wide
simulations and cyber drills (Kapadia et al., 2020).
This multi-faceted approach has enabled JPMorgan
Chase to reduce incident recovery times, improve
regulatory compliance posture, and enhance client
trust. Their resilience engineering program serves as a
benchmark for large-scale financial institutions
navigating operational complexity and regulatory
scrutiny.
8.
Future Trends
New resilience engineering themes are emerging as
financial systems evolve in the fast-paced, digital global
economy. These trends use modern technologies,
decentralized systems, and improved cybersecurity to
identify and reduce operational hazards.
•
AI-Driven Resilience:
AI and ML models are being
used to detect anomalies, predict system
degradations, and initiate proactive recovery. AI
systems can forecast problems and optimize
incident response by studying previous performance
data and real-time telemetry. ML-based models in
observability stacks have improved uptime and
service
predictability
for
HSBC
and
ING
(Deloitte,2022).
•
Zero Trust Architectures:
ZTS principles assume no
default trust for users or components. Identity
verification,
least-privilege
access,
micro
segmentation, and continuous monitoring are these
architectures. In resilience, ZTS reduces breach blast
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radius and prevents system failure (Forrester
Research, 2021).
•
Edge Resilience:
Real-time financial applications like
mobile payments, decentralized exchanges, and
market data feeds are driving computers to the
edge. Edge resilience ensures important services can
continue when core infrastructure fails. Mastercard
is investigating edge compute nodes for fraud
detection to improve speed and reliability in
intermittently connected regions (Mastercard
Labs,2022).
•
Blockchain
for
Settlements
and
Resilient
Infrastructure:
DLT offers transparent, immutable,
and auditable transaction trails. It eliminates central
clearinghouse dependence and improves financial
transaction fault tolerance. JPMorgan's Onyx and
the European Central Bank's CBDCs investigate DLT
for interbank settlement operational continuity
(BIS,2022).
•
RaC (Resilience-as-Code):
RaC automates fault
injection, observability instrumentation, and
remediation logic, inspired by Infrastructure-as-
Code. Chaos Toolkit and Litmus provide repeatable
and scalable resilience testing in CI/CD processes.
These trends indicate a purposeful endeavor to build
resilience into digital finance platforms. These
innovations can give financial institutions operational
resilience and competitive competitiveness in a
regulated and rapidly changing ecosystem.
9.
CONCLUSION
Financial firms trying to keep service continuity among
growing complexity, cyber risks, market volatility, and
regulatory pressure now mostly rely on resilience
engineering as their paradigm. Preventive and adaptive
measures must take front stage as conventional
catastrophe recovery approaches show inadequate for
current operational needs.
Resilience is about engineering systems to predict,
absorb, and respond to real-time disturbances, not only
about redundancy or recovery, but this paper has also
demonstrated. Foundational skills that fit both
engineering and regulatory objectives are observability,
redundancy, elasticity, and a blameless culture
(Hollnagel, 2011; Beyer et al., 2016).
Microservices, event-driven pipelines, immutable
infrastructure support system fault separation and quick
recovery, architectural patterns include multi-region
deployments, microservices support system fault
isolation and rapid recovery (Burns et al., 2016).
Through constant monitoring, error budgeting, fault
injection, and cross-functional preparedness (basiri et
al., 2016) operational approaches like SRE and Chaos
Engineering further incorporate resilience into daily
operations.
Resilience has been underlined as a non-negotiable
cornerstone of financial stability (FCA, 2021; European
Commission, 2022) by regulatory frameworks including
the FCA's IBS model, DORA, FFIEC advice, and BCBS
regulations. Including resilience engineering across
architecture, operations, and compliance, forward-
looking companies including JPMorgan Chase, Capital
One, and Goldman Sachs show how significantly uptime,
customer trust, and audit readiness improve (Kharif,
2022).
Adoption of AI-driven analytics, Zero Trust models,
blockchain infrastructure, and Resilience-as-Code
techniques is poised to reshape the boundaries of
resilient financial ecosystems going forward. These
developments point to a change from passive fault
tolerance to intelligent, self-healing, compliance-aware
systems.
Financial firms may not only survive but also flourish in
a turbulent and linked world by including resilience
engineering holistically
—
from design and testing to
operations and regulation.
REFERENCES
Basiri, A., et al. (2016).
Chaos Engineering
. Netflix Tech
Blog.
Beyer, B., Jones, C., Petoff, J., & Murphy, N. R. (2016).
Site Reliability Engineering: How Google Runs Production
Systems
. O’Reilly Media.
BIS. (2022).
CBDCs and the Future of Financial Systems
.
Bank for International Settlements.
Burns, B., Grant, B., Oppenheimer, D., Brewer, E., &
Wilkes, J. (2016). Borg, Omega, and Kubernetes.
Communications of the ACM
, 59(5), 50
–
57.
Capital One. (2021).
Modernizing Cloud Infrastructure
for Resilience
. Internal Engineering Report.
Chen, Z., et al. (2021). Event-Driven Architecture for
Financial Services.
IEEE Software
, 38(4), 30
–
37.
CNBC. (2022).
How JPMorgan is Building a Cloud-Native
Bank
. Retrieved from https://www.cnbc.com
The American Journal of Engineering and Technology
61
https://www.theamericanjournals.com/index.php/tajet
Deloitte. (2022).
AI in Financial Services: A New Era of
Predictive Resilience
. Deloitte Insights.
European Commission. (2022).
Digital Operational
Resilience Act (DORA)
.
FCA. (2021).
Building Operational Resilience: Policy
Statement
. Financial Conduct Authority.
FFIEC. (2021).
Business Continuity Management Booklet
.
Federal Financial Institutions Examination Council.
Forrester Research. (2021).
Zero Trust Architecture and
Resilience
. Forrester.
Goldman Sachs Engineering. (2020).
How Marcus Uses
Microservices for Stability and Speed
. GS Tech Blog.
Gremlin. (2021).
Chaos Engineering Use Cases in
Finance
. Retrieved from https://www.gremlin.com
Hollnagel, E. (2011).
Resilience Engineering in Practice: A
Guidebook
. CRC Press.
Kapadia, S., et al. (2020).
Operational Resilience and
Financial Stability
. Bank of England.
Kharif, O. (2022).
How JPMorgan Is Building a Resilient
Cloud Architecture
. Bloomberg Tech.
Mastercard Labs. (2022).
Edge Compute in Financial
Fraud Detection
. Mastercard Engineering.
O'Reilly Media. (2020).
Adopting SRE in Financial
Enterprises
.
Woods, D. D. (2006). Essential Characteristics of
Resilience. In
Resilience Engineering: Concepts and
Precepts
, Ashgate Publishing.
