Risk Assessment and Response Strategies: Theory and Cases

Qingyang Zhang

School of International Economics and Trade, Central University of Economics, Beijing 100081, China

Keywords: Risk Assessment, Delphi Method, COSO Framework, Risk Transfer, Internal Audit.

Abstract: In the contemporary business environment deeply intertwined with globalization and digitalization, the risks

faced by enterprises exhibit characteristics of complexity, dynamics, and cross-border transmission, making

traditional risk management models difficult to cope with new challenges such as supply chain disruptions

and technological disruptions. This study aims to construct a dual-track model for risk assessment that

integrates qualitative insights and quantitative analysis, as well as a three-dimensional response framework

adapted to different risk types. Through a hybrid model of the Delphi method and Adaptive Support Vector

Machine (ASVM), combined with the COSO internal control framework and blockchain technology, a

systematic analysis of multi-industry cases in manufacturing, finance, and agriculture reveals that structured

expert feedback can improve the accuracy of emerging risk identification , while the ASVM model improves

the accuracy of default prediction for GEM listed companies by 17% compared to traditional methods. The

study further reveals that the effectiveness of risk avoidance strategies depends on the flexible application of

real options theory, risk transfer tools need to balance compliance and moderation, and digital risk control

systems can increase risk response speed by 80%. This study breaks through the single-dimensional limitation

of traditional risk assessment and proposes a "dual-source driven model of internal data-external signals",

providing a theoretical framework and operational guidelines for enterprises to build a resilient risk

management system. Policy recommendations focus on the establishment of cross-enterprise risk co-

governance mechanisms and AI ethics norms.

1 INTRODUCTION

Under the dual impact of globalization and

digitalization, the risks faced by enterprises have

become increasingly complex, dynamic, and cross-

border transmissible. Whether supply chain

disruptions, technological disruptions, compliance

pressures, or climate risks, building a flexible and

robust risk management system has become a key

challenge for corporate sustainable development (Li

et al., 2022; COSO, 2017). For example, during the

supply chain crisis triggered by the 2020 pandemic,

enterprises using dynamic risk assessment models

identified supply chain disruption risks 4 weeks

earlier than traditional enterprises, with an 18%

increase in inventory turnover (Li et al., 2022).

This study combines theories from management,

finance, and information science to propose a "dual-

track model" (qualitative + quantitative) for risk

assessment and a "three-dimensional framework"

(avoidance, transfer, mitigation) for response

strategies through comparative analysis of multiple

cases. At the same time, it explores how to use the

COSO internal control framework and blockchain

technology to achieve the modernization of risk

governance. The research data covers multiple

industries including manufacturing, finance, and

agriculture, and references classic theories (Dalkey &

Helmer, 1963) and cutting-edge empirical research

(Jorion, 2006; COSI, 2013; International Standard on

Auditing, 2019; Trigeorgis, 2016; Weber, 2017),

aiming to provide enterprises with a set of

implementable risk management methodologies.

The remaining parts of this paper are arranged as

follows: Section 2 constructs a "double-layer

filtering-multi-dimensional modeling-dynamic

calibration" risk assessment system to analyze the

collaborative logic of qualitative and quantitative

methods; Section 3 proposes a three-dimensional risk

response strategy based on the COSO framework and

discusses strategy adaptability by combining cases

such as Galanz and Hainan Rubber; Section 4

analyzes the dual pillar role of internal control and

audit, focusing on the empowerment of digital

transformation for risk governance; Section 5

Zhang, Q.

Risk Assessment and Response Strategies: Theory and Cases.

DOI: 10.5220/0014366800004718

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 2nd International Conference on Engineering Management, Information Technology and Intelligence (EMITI 2025), pages 583-588

ISBN: 978-989-758-792-4

583

identifies the core challenges of current risk

management and prospects future research directions;

finally, the conclusions summarize the research

findings and emphasize the strategic value of

dynamic closed-loop systems in uncertain

environments.

2 RISK ASSESSMENT SYSTEM:

FROM EXPERT EXPERIENCE

TO DATA-DRIVEN

The core task of risk assessment is to answer two

questions: "Which risks need attention?" and "How

severe are the risks?" This paper proposes a "double-

layer filtering-multi-dimensional modeling-dynamic

calibration" assessment framework, combining

expert experience and algorithmic intelligence to

achieve a more comprehensive characterization of

risks.

2.1 Qualitative Assessment

In areas with insufficient data or emerging risks,

qualitative assessment remains crucial. The Delphi

method is a classic expert consensus method that

reduces group bias and improves prediction accuracy

through anonymous feedback and iterative

adjustments (Dalkey & Helmer, 1963). This method

originated from a 1960s technology forecasting

experiment by the RAND Corporation, where Dalkey

and Helmer (1963) first used it to simulate Soviet

strategic bombing assessments of U.S. industrial

targets. In the experiment, experts' initial estimated

range for the number of bombs was 50-5,000, which

converged to 167-360 after five rounds of anonymous

feedback, with the maximum-minimum ratio

dropping from 100:1 to 2:1, highlighting the role of

structured feedback mechanisms in curbing

groupthink (Dalkey & Helmer, 1963).

Modern enterprises often combine the Delphi

method with fuzzy mathematics. For example, XA

City Tobacco Company adopted a four-layer risk

assessment model (strategic, industry, operational,

compliance), and through three rounds of expert

opinion solicitation (15 cross-disciplinary experts

including policy researchers, risk analysts, and

corporate compliance officers), each round required

experts to score risk factors on a 1-10 scale, and fuzzy

analytic hierarchy process (FAHP) was used to

calculate weights, with the "tobacco control policy

risk" in the strategic layer weighing 0.32,

significantly higher than other factors, finally

establishing a three-level early warning mechanism

(high risk >0.6, medium risk 0.3-0.6, low risk <0.3)

(Cox, 2008). This method improved the enterprise's

resource allocation efficiency in high-impact risk

areas such as regulatory policy changes by 40%, but

requires mechanisms such as expert industry

experience spanning ≥ 5 years and transparent

feedback processes to reduce subjectivity (Dalkey &

Helmer, 1963).

The risk matrix is another commonly used tool for

prioritizing risks through two-dimensional

classification of "probability-impact". However,

traditional matrices ignore non-linear interactions

between risks, and Cox (2008) recommended adding

"recovery difficulty" as a third dimension (Cox,

2008). For example, an automobile manufacturer

positioned the "chip shortage" risk as (high

probability 0.8, high impact 0.9, high recovery

difficulty 0.7), triggering a level-one response

mechanism. By adding three new chip foundries

(including TSMC, Intel, and Samsung) and

increasing safety stock from 30 days to 120 days, the

risk of production line shutdowns was reduced from

45% to 12% (Cox, 2008).

2.2 Quantitative Assessment

Quantitative methods rely on data and algorithms to

provide more objective risk metrics. Value at Risk

(VaR) is a standard tool in the financial industry for

calculating maximum losses at specified confidence

levels through historical simulation or Monte Carlo

methods (Jorion, 2006). However, VaR exposed the

flaw of underestimating tail risks during the 2008

financial crisis, and Acerbi (2013) proposed the

Expected Shortfall (ES) model, which improves

prediction accuracy in extreme scenarios by 30% by

calculating the mean of tail losses (Acerbi, 2013).

In non-financial fields, the Adaptive Support

Vector Machine (ASVM) has performed

outstandingly. A study on GEM listed companies

found that ASVM, combining financial indicators

(current ratio, debt-to-asset ratio), market data

(volatility, market value-to-revenue ratio), and ESG

information (number of patents, carbon emission

intensity), uses a radial basis function (RBF) for non-

linear mapping and optimizes penalty parameter C

and kernel parameter γ through cross-validation,

improving default prediction accuracy to 89%, far

exceeding the 72% of traditional logistic regression

(Li, et al., 2022). A technology company embedded

ASVM into its supply chain system to real-time

monitor 12 indicators such as supplier delivery delay

rate (weight 30%), quality qualification rate (25%),

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and capacity utilization rate (20%), setting a Z-score

standardized data preprocessing process that

automatically triggers procurement share adjustments

when the comprehensive risk index exceeds the

threshold of 0.7, reducing critical component supply

disruption risks by 34% (Li et al., 2022).

In data-scarce fields (such as emerging

technology risks), expert experience remains a key

supplement to algorithms. A new energy vehicle

enterprise collected 20 experts' predictions on the

mass production time of solid-state batteries (5-7

years, 60% probability) and cost decline trends (15%-

20% annual decline) through the Delphi method as

prior data for the ASVM model, and then combined

empirical data such as annual R&D investment

growth rate (25%) and annual patent applications (80)

for correction, using Bayesian networks to update

prior distributions, finally predicting the technology

substitution critical point at 5.8 years and accordingly

adding ¥300 million in R&D budget to the solid-state

battery R&D project (Li et al., 2022; Dalkey &

Helmer, 1963).

3 RISK RESPONSE

STRATEGIES:

THREE-DIMENSIONAL

FRAMEWORK AND FLEXIBLE

ADAPTATION

The core of risk response is to balance cost,

effectiveness, and resilience, i.e., selecting optimal

strategy combinations under resource constraints

while enhancing system risk resistance. This paper

summarizes three main strategies:

3.1 Risk Avoidance: Strategic

Contraction and Active

Withdrawal

The essence of avoidance is to proactively stay away

from high-risk and low-return areas, with decision-

making requiring the evaluation of opportunity costs

using real options theory (Trigeorgis, 2016). Take

Galanz's acquisition of Whirlpool Europe as an

example. Facing an 80% debt financing proposal

from an investment bank (annual interest rate 5.5%,

term 5 years), management chose to acquire gradually

with its own funds (annual retained earnings growth

of 12%, cumulative retained earnings of €280

million), although extending the transaction cycle to

24 months, it avoided solvency pressures caused by

euro interest rate fluctuations. Trigeorgis (2016)'s real

options model shows that this "waiting strategy"

preserved flexibility to respond to exchange rate

fluctuations (annual euro-RMB volatility of 3.2%)

and market demand changes, with a value equivalent

to a €120 million strategic option premium, higher

than the net present value of immediate acquisition of

€98 million (Trigeorgis, 2016).

Industry lifecycle theory provides practical

guidance for avoidance strategies. A traditional

automobile enterprise shut down three engine

factories during the decline of the fuel vehicle market

(annual market share decline of 8%) and shifted to EV

R&D, this decision aligns with the "abandonment

option" logic in real options theory—by actively

abandoning annual sunk costs of ¥500 million in fuel

vehicle production capacity, it gained a first-mover

advantage in the new energy track, with projected EV

business revenue exceeding ¥3 billion within 5 years

(Trigeorgis, 2016). In the chemical industry with

volatile policies, an enterprise set a ceiling of no more

than 15% of revenue for high-risk businesses,

reducing environmental penalty risks by 58% and

annual compliance costs by ¥20 million (Trigeorgis,

2016).

3.2 Risk Transfer: Contractual Tools

and Compliance Balance

The core of risk transfer is to share risks with third

parties through contractual arrangements, but

attention must be paid to tool adaptability and

compliance. Hainan Rubber Group adopted a

composite "insurance + futures" model: purchasing

rubber price index insurance (covering 20%

downside price risk, premium rate 3%), while

establishing short positions in the Shanghai Futures

Exchange RU2309 contracts to hedge production

volatility, holding 70% of the expected production

volume in 2022, with basis fluctuations controlled

within ±500 yuan/ton, reducing revenue volatility

from 25% to 12%, despite bearing a 5% hedging cost

(insurance premiums + futures transaction fees)

(Trigeorgis, 2016).

Excessive use of derivatives can lead to reverse

risks. During the 2008 financial crisis, an airline

signed fuel call options with a notional amount of $1

billion (strike price $140/barrel), and when oil prices

plummeted to $30/barrel, the option fair value loss

exceeded ¥1 billion, exposing the mismatch between

risk transfer tools and market expectations (COSO,

2013). The COSO (2013) internal control framework

points out that such risks arise from the lack of

dynamic assessment of tool leverage (this option had

Risk Assessment and Response Strategies: Theory and Cases

585

a 5x leverage) and market trends, requiring

enterprises to establish a "risk exposure-market

volatility" dynamic matching mechanism (COSO,

2013).

Installment payments are a common risk-sharing

mechanism in M&A transactions. Galanz adopted a

"60% down payment (€450 million) + 40% earn-out"

structure in its Whirlpool acquisition: the remaining

payment is made in installments based on the three-

year post-acquisition EBITDA targets (thresholds of

€120 million, €150 million, and €180 million), with a

30% performance compensation clause stipulating

that the seller must compensate 1.5 times the shortfall

if actual EBITDA is less than 80% of the target,

transferring part of the valuation and operational risks

to the seller (International Standard on Auditing,

2019). The technology industry often uses "milestone

payments" in M&A, such as a pharmaceutical

company agreeing to pay 30% of the final payment

($120 million) only after the acquirer successfully

completes Phase III clinical trials (45% probability)

(Trigeorgis, 2016).

3.3 Risk Mitigation: Process

Embedding and Technology

Enablement

Risk mitigation reduces the probability or impact of

risks through internal control optimization, with its

effectiveness dependent on deep integration with

business processes (COSO, 2013). China Feihe Dairy

reconstructed its milk source supply risk management

system guided by the COSO framework: at the

control environment level, it appointed a Chief

Compliance Officer (CCO) to report directly to the

audit committee and established a supplier ethics

review checklist covering 10 indicators such as labor

standards and environmental compliance, requiring

suppliers to submit SA8000 certification and

ISO14001 reports annually, with non-compliant

suppliers added to a blacklist; at the risk assessment

level, it developed a three-dimensional monitoring

model for raw milk prices, pandemic policies, and

transportation costs. In Q3 2022, when a regional

pandemic lockdown triggered a risk level upgrade

(low → medium), it proactively activated an

emergency procurement plan (activating three

backup pastures) and switching transportation routes

to highways), reducing supply chain disruption losses

by 65%; at the control activities level, through the

separation of supplier approval and contract signing

authorities and blockchain milk source traceability

technology (using Ant Chain BaaS platform to update

milk source data every 2 hours), it reduced

procurement fraud rates from 7% to 4.2%

(International Standard on Auditing, 2019).

Digital technologies significantly enhance risk

mitigation efficiency. An international

pharmaceutical company uploaded its full lifecycle

drug data to the blockchain, allowing auditors to

automatically verify batch compliance through smart

contracts (e.g., production date to expiration date ≥

18 months), reducing audit cycles from 15 days to 3

days and false transaction risks by 50% (Weber,

2019). A food enterprise used natural language

processing (NLP) technology to analyze over 100,000

daily social media posts, employing an LSTM neural

network to train a sentiment classification model with

89% accuracy. When negative mentions of "raw milk

antibiotic residues" increased by 120% month-on-

month, the system automatically triggered quality

department surprise inspections of 37 suppliers

(covering 30 indicators such as raw milk microbes

and heavy metals), containing reputational risks at an

early stage, with response speed improved by 80%

compared to traditional models (Weber, 2017).

4 INTERNAL CONTROL AND

AUDIT: DUAL PILLARS OF

RISK GOVERNANCE

4.1 Dynamic Evolution of Internal

Control Systems

The COSO Enterprise Risk Management Framework

(2017) emphasizes the strategic orientation of risk

governance, requiring risk assessment to be

embedded in the strategic formulation process

(Trigeorgis, 2016). When planning capacity

expansion, a new energy enterprise simulated three

future scenarios through scenario analysis: (1)

sustained policy subsidies (30% probability, subsidy

rate remains 15%), corresponding to an aggressive

capacity layout (building three new factories with a

¥2 billion investment); (2) breakthroughs in solid-

state battery technology (50% probability, energy

density increases to 500Wh/kg), triggering

technology reserves and supply chain adjustments

(increasing R&D investment by ¥500 million and

signing exclusive procurement agreements with

CATL); (3) implementation of carbon tariffs (20%

probability, tax rate 10€/ton CO₂), initiating carbon

cost accounting and green power procurement plans

(installing a 100MW photovoltaic power plant,

improving carbon footprint accounting accuracy to

0.1kgCO₂/kg product). For each scenario, the

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enterprise designed differentiated control solutions,

such as a technology R&D reserve system (3% of

revenue), supplier regionalization (building two new

raw material bases in Southeast Asia), and a carbon

footprint tracking system (introducing SAP

Sustainability Control Tower) (International,

Standard on Auditing, 2019).

4.2 Risk Assessment and Digital

Transformation: Dynamic

Monitoring and Real-Time

Response

China Feihe designated milk source supply risk as a

top-level risk and built a dynamic monitoring system

covering "prices-policies-public opinion". Through

IoT sensors (deployed in 12 major global milk source

regions), it real-time collected raw milk price data,

combined with daily customs policy updates (OCR

recognition of PDF policy documents) and traffic

control information (accessing Gaode Maps API for

real-time traffic conditions), to establish a

multivariate early warning model (including ARIMA

time series and GARCH volatility models). In Q2

2022, when the model detected signals of export

restrictions due to a pandemic in a milk source

country (export volume decreased by 40% month-on-

month), it proactively switched to backup suppliers

four weeks in advance (increasing Australian milk

source share from 15% to 40%), reducing supply

chain disruption risks by 65%.

In digital transformation, the enterprise

introduced a big data public opinion monitoring

system (purchased from Weiyuqing Platform), using

sentiment analysis algorithms (based on BERT pre-

trained models) to evaluate public opinion trends.

When mentions of "raw milk quality complaints"

surged by 120% in a certain quarter of 2023, the

system automatically generated risk briefings

(including Top 10 high-frequency negative keywords)

and sentiment polarity distribution), triggering

quality departments to conduct surprise inspections of

suppliers, testing raw milk from 37 suppliers using

liquid chromatography-mass spectrometry for 18

antibiotic residues, completing all sample analysis

within 3 days, reducing potential crisis handling time

to within 24 hours (Weber, 2017). This "data

collection-intelligent analysis-instant response"

closed loop improved risk identification speed from

monthly reports to real-time warnings, extending key

decision-making windows by 3 days (Weber, 2017).

Intelligent audit tools are reshaping risk

governance landscapes. Walmart achieved supply

chain data traceability through blockchain evidence

storage technology (using Hyperledger Fabric

architecture), with auditors automatically matching

orders, waybills, and invoices through smart contracts

(three-way matching accuracy 99%), reducing

sampling errors from 8% to 3% (Weber, 2017). A

bank used random forest algorithms to analyze

customer transaction data, setting 12 risk

characteristics such as "cross-border transfer

frequency >5 times/day with no trade background",

optimizing model parameters through grid search,

improving the accuracy of identifying potential

money laundering activities by 45% compared to

traditional rule engines, intercepting 237 suspicious

transactions involving $89 million in 2023 (Weber,

2017). Before constructing its German factory, Tesla

identified EU carbon tariff (CBAM) risks through

SWOT analysis, designing special audit procedures

(commissioning third-party agencies to calculate

Scope 1-3 emissions) to evaluate carbon emission

accounting compliance and avoid potential fines

exceeding €100 million (Weber, 2017).

5 CHALLENGES AND FUTURE

RESEARCH DIRECTIONS

5.1 Core Challenges in Current Risk

Management

Data silos hinder the collaborative efficiency between

qualitative and quantitative assessments, making it

difficult to effectively translate expert experience into

algorithm inputs (e.g., Delphi results require manual

encoding as numerical features); emerging risks such

as AI ethics and quantum computing security lack

mature assessment frameworks (existing models have

insufficient quantitative indicators for algorithmic

bias); cross-enterprise supply chain risk transmission

requires the establishment of upstream-downstream

collaborative mechanisms, but Gartner (2023)

surveys show that only 28% of enterprises have real-

time risk data interoperability with primary suppliers

(most still rely on Excel email transmission); digital

tools introduce new risks such as algorithmic bias and

cybersecurity, with one risk control model

underestimating SME loan approval rates by 15% due

to insufficient SME samples (accounting for <10%)

in training data (Li et al., 2022; Weber, 2017).

5.2 Future Research Agenda

Mixed reality (MR) technology can simulate the

impact of technological disruptions through virtual

Risk Assessment and Response Strategies: Theory and Cases

587

reality scenarios (e.g., simulating job reduction risks

from AI customer service replacing humans),

enhancing the immersive experience of expert

judgment (expected to improve evaluation efficiency

by 50%); real-time risk dashboards need to integrate

dynamic Delphi feedback and ASVM algorithms,

developing WebSocket real-time communication

interfaces to achieve second-level risk rating updates

on management mobile apps; ESG risk capitalization

research needs to explore quantitative correlation

models between carbon emissions and credit ratings

(e.g., constructing carbon intensity-default

probability regression equations); blockchain-driven

industry alliance chains can achieve cross-enterprise

supply chain risk warning transmission, with pilot

data from an automotive industry alliance showing

that information sharing advanced disruption

warnings by 72 hours and increased inventory

turnover by 12% (Weber, 2017).

6 CONCLUSION

Effective risk management is a critical fusion of

scientific rationality and managerial art. The

structured, multi-round anonymous processes of the

Delphi method rigorously ensure the

comprehensiveness of risk identification, while the

algorithmic precision of VaR and ASVM underpins

the accuracy of risk quantification. Concurrently, the

implementation quality of the COSO framework and

the deep integration of blockchain technology,

leveraging its immutability and transparency, directly

determine the practical effectiveness of risk responses.

Illustrative cases abound: Galanz's strategic

avoidance, Hainan Rubber's innovative transfer

mechanisms, China Feihe's meticulous process-based

mitigation, and Tesla's cutting-edge intelligent audit

practices. Collectively, they underscore the necessity

for enterprises to establish a dynamic, closed-loop

system encompassing "assessment-response-

monitoring". This entails enhancing evaluation

accuracy through hybrid models blending expert

intuition with data-driven intelligence, optimizing

risk exposure through diversified, multi-layered

strategies, and driving governance iteration via

advanced digital tools.

In today's era of accelerated technological

disruption, corporate risk management is decisively

shifting from "passive response" towards "active

prediction". Looking ahead, the proliferation of

generative AI and the Internet of Things will further

empower this evolution: risk assessment will gain

unprecedented foresight, responses will become

inherently more resilient and adaptive, and

governance models will evolve towards greater

ecological integration and collaboration. To thrive,

enterprises must embrace technological

advancements with an open mindset while steadfastly

adhering to risk management's core essence:

employing scientific mechanisms to dynamically

balance risks and rewards, thereby forging

sustainable competitive advantages within an

inherently uncertain global landscape.

REFERENCES

Li, X., et al. (2022). Dynamic risk assessment of GEM

listed companies based on ASVM model. Journal of

Financial Risk Management, 10(3), 45–63.

COSO. (2017). Enterprise Risk Management—Integrated

Framework. Committee of Sponsoring Organizations

of the Treadway Commission.

Dalkey, N., & Helmer, O. (1963). An experimental

application of the Delphi method to the use of experts.

Management Science, 9(3), 458–467.

Cox, L. A. (2008). What’s wrong with risk matrices? Risk

Analysis, 28(2), 497–512.

Jorion, P. (2006). Value at Risk: The New Benchmark for

Managing Financial Risk. McGraw-Hill.

COSO. (2013). Internal Control—Integrated Framework.

Committee of Sponsoring Organizations of the

Treadway Commission.

International Standard on Auditing (ISA) 315. (2019).

Identifying and Assessing the Risks of Material

Misstatement through Understanding the Entity and Its

Environment.

Trigeorgis, L. (2016). Real Options Theory: Managerial

Flexibility and Strategy in Resource Allocation. MIT

Press.

Weber, R. (2017). Blockchain technology in accounting

and auditing: The emergence of trust. Accounting

Horizons, 31(3), 383–403.

Acerbi, C. (2013). Expected shortfall: A coherent

alternative to VaR. Journal of Banking & Finance,

37(11), 4301–4314.

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