Using Artificial Intelligence to Predict Financial and Administrative Risks: An Applied Study at the International Development Bank for Investment and Finance (2015–2024) (International Journal of Financial Administrative and Economic Sciences)
Summary
- The study evaluates the impact of deploying artificial intelligence techniques to predict financial and administrative risks at the International Development Bank for Investment and Finance over the period 2015–2024.p.1
- Machine learning utilization within the bank expanded from 25% in 2015 to 75% in 2024.p.7
- Deep learning adoption in internal bank applications grew from 5% in 2015 to 50% in 2024.p.7
- Credit risk metrics recorded an increase from 20% in 2015 to 42% in 2024 along with credit portfolio growth.p.8
- Operational risk levels shifted from 18% in 2015 to 32% in 2024 as bank operations multiplied.p.8
- Statistical regression indicates that each 10% increase in AI adoption corresponds to approximately a 5-6% improvement in risk prediction capability.p.9
Key insights
- Risk prediction model utilization in the bank increased from 15% in 2015 to 60% in 2024.p.7
- The correlation coefficient between AI implementation and financial risk prediction capability strengthened from 0.85 in 2015 to 0.97 in 2024.p.9
- The correlation coefficient between AI usage and administrative risk prediction increased from 0.78 in 2015 to 0.91 in 2024.p.9
- Market risk exposure metrics moved from 15% in 2015 to 35% in 2024.p.8
- Liquidity risk metrics rose from 10% in 2015 to 28% in 2024.p.8
- Governance risk indicators moved from 17% in 2015 to 28% in 2024.p.8
- Natural language processing usage inside the institution expanded from 10% in 2015 to 55% in 2024.p.7
Topics and entities
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