Transform complex financial data into strategic insights that empower your investment, budgeting and growth decisions.
Real cases where AI empowers your decisions
Narrative: A technology company developed an early warning system analyzing up to 80,000 items daily to flag customers with signs of spoilage, while a global bank applied AI to significantly reduce fraud cases.
Challenge: Financial risks not detected in time cause losses in the millions, late identification of credit problems prevents preventive actions, and fraud has a direct impact on the bottom line.
Benefit: Up to 15% reduction in delinquency rate, risk detection much earlier than with manual monitoring, over 50% reduction in fraudulent transaction losses, and better asset protection with fewer false positives.
Narrative: A European bank used AI analytics to segment customers by margin generated, identifying that 20% of customers contributed 80% of profit, while an e-commerce company uses algorithms to identify and engage large shoppers.
Challenge: Lack of visibility into actual profitability per customer prevents efficient targeting of resources, overall strategies do not maximize the value of the most profitable segments, and resources are wasted on unprofitable customers.
Benefit: 15% increase in revenue per customer in the top segment, 35% growth in revenue from more profitable customers, 20% increase in retention, 25% improvement in satisfaction, and optimization of commercial ROI.
Narrative: A global consulting firm documented cases where integrating AI to automate financial data flows halved report preparation time, while a manufacturing company adopted a platform to reduce its monthly close from 10 to 5 days.
Challenge: Manual reporting consumes valuable time of the finance team, manual processes are prone to human error, and slow reporting delays critical decisions.
Benefit: Halving report preparation time, saving 1-2 full days per cycle, freeing up 5 man-days per month, drastically reducing human error, saving up to 40% of team time, and increased confidence in reported numbers.
Narrative: A financial services company keeps fraud to just 0.1% of transactions using AI with more than 500 variables per transaction, while a European bank recovered €2 million in its first year by identifying atypical expenses automatically.
Challenge: Fraud causes significant direct losses, improper expenditures go undetected in manual reviews, and late detection amplifies the negative financial impact.
Benefit: Maintaining fraud in only 0.1% of transactions, reducing 20% in legitimate transactions rejected in error, recovering €2 million in misapplied charges, preventing $20 million annually in check fraud, and recovering $800k annually for duplicate payments.
Narrative: A specialized consulting firm developed a platform that combines traditional benchmarking with generative AI, while studies show that companies that maximize "return on talent" obtain up to 300% more revenue per employee.
Challenge: The lack of accurate benchmarking prevents the identification of performance gaps, companies do not know their real position vis-à-vis competitors, and strategic decisions are made without a market context.
Benefit: Potential for 40% of operating cost reduction, 44% of productivity improvement over peers, achievement of top-quartile company efficiency levels, and better calibrated strategic decisions to achieve leading-edge efficiencies.
Narrative: An oil company employs AI to simulate hundreds of pricing scenarios, saving billions by quickly adjusting capex and expenses, while a Dutch bank developed credit portfolio simulation models.
Challenge: Market uncertainty generates unforeseen risks, the lack of scenario simulation prevents adequate preparation, and crises surprise companies without contingency plans.
Benefit: Savings of billions by adjusting quickly to oil downturns, ~1 percentage point increase in pre-crisis capital ratio, 20-30% reductions in idle liquidity cost, and better navigation in crises such as 2020-2021.
Narrative: An e-commerce company updates millions of prices a day with AI, contributing to around 25% increase in revenue after implementation, while a Baltic airline reported 2-3% increase in revenue per seat using AI.
Challenge: Static pricing misses opportunities to maximize revenue, competition with dynamic pricing outperforms traditional models, and the lack of real-time adjustments limits value capture.
Benefit: ~25% increase in revenue after implementation, 1-8% revenue lift on average, ~10% improvement in profit margins, 2-3% increase in revenue per available seat, and 10% improvement in e-commerce conversion rate.
Narrative: An industrial conglomerate integrated AI into its capital planning to filter investments and prioritize those that exceed its hurdle rate, while a logistics company uses digital twins to test operational changes before implementing them.
Challenge: Strategic decisions are made without full visibility of consequences, lack of prior simulation can lead to unprofitable investments, and uncalculated impacts generate negative surprises.
Benefit: Increase of ~$1 billion in aggregate net present value in project pipeline, avoidance of capital expenditures of ~$100 million in non-viable investments, savings of an additional $200-300 million by avoiding unprofitable changes, and drastic improvement in quality of strategic decisions.
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Narrative: A multinational food company implemented machine learning to refine its demand forecasts, while several industry studies demonstrate the transformative impact of AI on inventory management.
Challenge: Companies face constant stock-outs that result in lost sales, product obsolescence due to excess inventory, forecasting errors that affect planning, and overworked planning teams.
Benefit: 30% reduction in lost sales due to stock-outs, 30% less product obsolescence, 20% less forecast errors, up to 50% less planning team workload, and decreased demand errors by 30-50% with logistics costs reduced by 10-40%.