How AI Makes Decisions

AI – you input, it outputs. At times, it's as if it's reading your mind, pulling out answers and insights you never knew existed with breathtaking efficiency and precision. But have you ever paused to wonder; how does AI reach these decisions? How has it evolved into a technology that leaves us in awe, to say the least?

AI's decision-making, from rule-based systems to data-digesting models, is powered by Machine Learning (ML) and Deep Learning (DL). We're bypassing the nitty-gritty of data collection and preprocessing to the crux of the matter: Modeling techniques. Let's get into it.

Where is decision making used?

First things first, where is decision making used? Although finance is often viewed as a very traditional field, it's been revolutionized by AI's diverse and impactful applications.

Take risk assessments, for instance, a staple of the industry. They once involved tedious number-crunching and educated guesses. However, AI models, with their ability to analyze market trends and customer behavior, can predict potential pitfalls and opportunities with unprecedented accuracy.

Additional aspects of banking and finance where AI’s influence is evident include fraud detection, where AI models recognize suspicious patterns in transaction data; algorithmic trading, where organizations are now capable of executing trades at the most favorable prices, making decisions faster than any human trader could.

Moreover, by considering a wide variety of factors, AI has brought a new level of precision to credit scoring, making it a more reliable indicator of creditworthiness.

All that said, one thing is undeniable: One of the most transformative applications of AI in finance has been in customer service. Personalized banking, powered by AI, has drastically improved the customer experience. Not only that, but it has also been found to save costs up to 4.4% per year. It provides personalized financial advice and services tailored to individual needs, changing the way customers interact with financial institutions.

AI uses a variety of techniques to reach outcomes, and the choice of model often depends on the task at hand.

Support Vector Machines (SVMs) find the best boundary to separate classes of data, deciding on where new data points belong. This capability to work effectively in high-dimensional spaces makes SVMs excellent for tasks like customer segmentation.

Gradient Boosting Machines (GBMs) build an ensemble of weak prediction models, usually decision trees, in a stage-wise fashion. Each subsequent model in the sequence is built to correct the errors of the previous model, leading to a final model that accurately makes decisions. Despite being sensitive to overfitting and more challenging to tune, the predictive accuracy of GBMs often makes them the go-to choice for credit risk modeling and predictive analytics.

XGBoost, a more efficient variant of GBMs, is optimized for computational efficiency and model performance. Its faster implementation of the gradient boosting algorithm makes it ideal to engage in underwriting for credit decisions and predicting investment returns.

Logistic Regression estimates the probability that a given input point belongs to a certain class. Its simplicity and the interpretability of its outputs make it a reliable tool for credit scoring and determining the likelihood of a customer defaulting on a loan.

For instance-based learning, the K-Nearest Neighbors (KNN) algorithm is a reliable choice. It classifies a data point based on how its neighbors are classified, making it especially useful in fraud detection systems where the behavior of fraudulent transactions is often like other fraudulent transactions.

Decision Trees, on the other hand, make decisions by splitting the data based on feature values. They embody simplicity and interpretability, handling both numerical and categorical data with ease. This makes them a popular choice for customer segmentation, risk assessment, and predicting the likelihood of customers using bank services.

Naive Bayes classifies based on Bayes’ Theorem with an assumption of independence among predictors. It's fast, easy to implement, and often used in text classification and sentiment analysis. Its efficiency with text data makes it a powerful tool for customer classification based on different features.

Spectral Analysis decomposes a signal into its constituent frequencies and uses the frequency spectrum to analyze the data and make decisions. Its effectiveness in identifying underlying structures or patterns in the data makes it particularly useful for uncovering periodicities and trends in time-series data, common in financial markets.

Finally, Splines offer flexibility in modeling non-linear relationships. They’re used in interest rate modeling and constructing yield curves. Their adaptability makes them a valuable tool in modeling and forecasting economic and financial time series data.

Understanding AI decision-making is crucial for leaders in the financial sector.

To make the most out of AI in finance, financial leaders need to do three things: Understand and choose the right models for each task, make sure they use AI ethically, and keep up with fast-paced tech changes. By doing this, they can tap into AI’s true power while keeping everything transparent and fair. This active approach is key for any financial institution to succeed in an AI-driven future.