One more interesting phase of decision-making is automation.
The decision-making systems increasingly at the forefront of business strategy-building-the AI systems use massive data in real time and offer predictive insights to help executives make significant decisions, e.g., whether to enter the market, develop a product, set a price. These systems will also keep improving their decision-making accuracy by machine learning after testing their predictions against actual events and trends, thereby making the organization all the more flexible and responsive.https://shaqoonline.com/shopify-store-to-generate/
Some examples of interesting applications include:
Credit Risk Assessment- AI algorithms are used by banks and financial institutions for credit risk assessment. The AI model considers transaction history, spending behavior, and social media activity while arriving at a credit score way beyond the traditional means.
Dynamic Pricing- AI is used by retailers and e-commerce businesses to adjust prices in real time based on demand changes, competitor pricing, and customer behavior. This allows for maximum pricing optimization and profit. https://shaqoonline.com/how-to-build-a-financial-literacy-foundation/
Fraud Detection- AI and ML can be selectively used to track individual activities undertaken in a transaction…
Fraud detection is one of the most powerful applications of AI in finance. AI systems detect anomalies by continuously monitoring financial transactions and flagging any suspicious activities that may signal fraud. These models learn what normal transactions look like and are also capable of recognizing patterns of irregularity. Because the classical methods used for detecting such frauds are extremely slow, the machine learning models would excel any human activity for detecting the irregularities. https://shaqoonline.com/b2b-marketing-strategy/
Advancements made in fraud detection are as follows:
Real-Time Transaction Monitoring: AI systems can inspect transactions in real-time, immediately flagging any activity that diverges from the user’s normal behavioral pattern, such as sudden large withdrawals or overseas spending.
Providing secure authentication through AI-powered biometric systems has now become an industry standard against unauthorized access to account control. Facial recognition, fingerprint scanning, and voice recognition are all being employed in conjunction with AI to provide secure identity verification.https://shaqoonline.com/cycling-for-mental-health/
Behavioral biometrics monitor user behavior (typing speed, navigation patterns, device holding) to allow AI models to identify possible fraudulent activity whenever a user based on an abnormal dataset behaves differently. This is noted even before a transaction is completed.
Issues and Ethical Concerns
AI may enhance efficiency and yet face major challenges.
The integration of AI in business and finance will deepen starting in 2025. This evolution towards more sophisticated automated decision-making, facilitated by quantum computing, will see a proactive rather than reactive stance in combating fraud by preventing it before it occurs. The downside of this dependence on AI implies that businesses will need to develop priority systems for data security, fairness, and transparency in their AI processes
