In the current economic climate, organisations must be data-driven to survive – whether that’s through minimising cost, identifying growth opportunities, or mitigating risks. Alex Hunt outlines some key data strategies that can help you succeed in an economic downturn.

What risks can data mitigate?

The current economic turndown is putting multiple pressures on business: inflation, rising interest rates, and labour shortages. You can measure the impact of these changing conditions through monitoring key risk indicators. These metrics use both internal and external data to provide insight into emerging risk conditions and show what your business needs to do to mitigate those risks.

Product or customer diversification

If one aspect of your business experiences a decline, analysing product or customer diversification will help to quantify any single points of failure, and provide opportunities for expanding into new markets or customers.

Be agile and prioritise essential products

Adapting your business strategy to evolving market conditions can help you sustain success. Analysis of product sales and margins will highlight which products are loss-making and not contributing to cash flow. This can be monitored on an ongoing basis to ensure changing input and delivery costs are reflected on a timely basis.

Leverage predictive analytics

During an economic downturn, predictive analytics can be a potent asset in foreseeing market trends and pinpointing avenues for expansion. Analysing purchasing data and demographic information, businesses can develop targeted marketing campaigns to customers, offering them promotions and discounts to keep them engaged with your brand.

Fraud risk mitigation

The risk of fraud tends to increase during a recession, but data analytics can play a crucial role in detecting it by examining large volumes of data to identify suspicious patterns or anomalies. 

Anomaly detection

By establishing baseline patterns of normal behaviour, data analytics can identify anomalies that may indicate fraudulent activities. Unusual transactions, unexpected changes in customer behaviour, or abnormal patterns in financial data can be flagged for further investigation.

Predictive modelling

Utilising historical data and machine learning algorithms, predictive modelling can identify patterns that are indicative of fraudulent behaviour. These models can be used to score transactions or activities based on their likelihood of being fraudulent, so you can prioritise investigations.

Real-time monitoring

By implementing real-time data analytics systems, organisations can continuously monitor transactions and activities, promptly detecting and responding to potential fraud. Real-time alerts can be triggered based on predefined rules or anomalous behaviour patterns.

Text and sentiment analysis

Data analytics techniques can be applied to text data, such as emails, customer reviews, or social media posts, to identify potentially fraudulent activities or indicators. Sentiment analysis can uncover negative sentiment or unusual language patterns associated with fraudulent behaviour.

Behavioural analysis

Data analytics can analyse user behaviour, such as navigation patterns, login activities, or transaction histories, to detect deviations from normal behaviour. Unusual behaviour or access patterns can raise red flags and prompt further investigation.

Cost controls

Maintaining financial stability for your business can be achieved by reducing superfluous expenditures. Using analytics can assist in identifying opportunities to optimise operations and eliminate non-essential expenses from your business. There are three key ways that data analytic techniques can evaluate internal controls and ensure expenditure is in line with your organisation's policies.

Statistical analysis

Use advanced routines across cost data to identify potential outliers that aren't compliant with internal policies, such as missing discounts, duplicates, incorrect unit pricing, or unapproved suppliers. 

Data visualisation

Utilise data visualisation to understand and analyse trends and behaviours of costs data. This will give an indication of how well internal controls are operating and if user behaviour is counter to the organisations ethics and policies. This can also be leveraged in future periods for benchmarking.

Cognitive detection

Use cognitive methods to understand if inappropriate items are being put forward as costs when they aren't allowable. By analysing descriptions of costs, you can detect keywords that suggest costs aren't appropriate or in line with internal policies. This could be employees claiming back items of a capital nature or personal expenses.

Preventing cash flow risks

It's important that businesses manage debtors and accurately assess accounts receivable on a monthly basis. Data analytics can be used to free up liquidity to maintain operational resilience, such as using prescriptive analytics to aid sensitivity analysis of daily forecasts, or to delay payments that are within pre-agreed payment terms. 

Cash flow forecasting

Data analytics can analyse historical cash flow data, sales trends, and payment patterns to create accurate cash-flow forecasts. By understanding future cash flow projections, businesses can better plan their expenses, identify potential shortfalls, and take proactive measures to address liquidity challenges.

Receivables and payables analysis

By analysing accounts receivable and accounts payable data, data analytics can help identify late payments, bottlenecks in the payment process, and potential delinquencies. This enables businesses to prioritise collections efforts, negotiate payment terms, and manage vendor relationships effectively to optimise cash inflow and outflow.

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Supply chain risk analytics

During times of economic uncertainty, the ability to cover the cost of goods can be challenging. Supply chains can involve complex arrangements reflecting the nature of commercial agreements in place. Therefore, it's important to negotiate with suppliers to make sure you're receiving favourable terms and that stock management is accurate.

Supplier risk assessment

Data analytics can assess supplier data, financial health, performance history, and market intelligence to evaluate supplier risks. By leveraging this information, businesses can proactively address underperforming suppliers, implement improvement initiatives, or consider alternative sourcing options to reduce supply chain risks. Analytics can be helpful to identify vulnerable suppliers and consider adjusting payment terms to ensure continuity from key suppliers. You can also use cognitive analytics to identify contracts with force majeure clauses to cover any SLA breaches, or identifying purchasing commitments that may now be onerous given the change in economic conditions.

Supplier and buyer analysis

Standardise primary characteristics that define the supplier and buyer population to identify trends and benchmarks for determining areas of risk, anomalous activity or lack of criteria required by commercial agreements, corporate policies, and local regulations.

Machine learning

Use artificial intelligence and machine learning to find patterns that can be indicators of risk, such as stock obsolescence, shrinkage, alternative suppliers, or unusual physical count variances. By considering various factors such as economic indicators, customer behaviour, and market dynamics, businesses can anticipate potential disruptions, adjust production and procurement strategies, and implement contingency plans to minimise risks.

Intelligent risk insights

Reducing payroll risks

Data analytics can be used to reduce payroll risks during a downturn by providing insights and facilitating effective risk management. 

Fraud detection

Data analytics can analyse payroll data to identify patterns and anomalies that may indicate fraudulent activities, such as ghost employees, unauthorised changes to employee records, or suspicious payroll transactions. By monitoring and analysing payroll data, businesses can promptly detect and mitigate payroll fraud risks.

Compliance monitoring

Data analytics can help ensure compliance with labour laws, tax regulations, and internal policies related to payroll. By analysing payroll data, businesses can identify potential compliance issues, such as incorrect wage calculations or non-compliance with overtime rules. This enables timely corrective actions and minimises legal and financial risks.

Time and attendance analysis

Data analytics can analyse time and attendance data to identify discrepancies, such as excessive overtime or unauthorised absences.

Error detection and resolution

Data analytics can identify errors or inconsistencies in payroll data, such as missing or incorrect entries, duplicate records, or calculation errors. By proactively detecting and resolving these issues, businesses can ensure accurate payroll processing and reduce the risk of financial penalties or employee dissatisfaction.

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Stay ahead of the curve

Data analytics can provide valuable insights, enabling you to make data-driven decisions, optimise operations, and adapt to changing market conditions in a downturn. Ensure you're optimising your data to not only prevent risks, but add commercial benefits.

For more insight and guidance, get in touch with Alex Hunt.


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