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Machine Learning
Learn what machine learning is, its types, and how it powers AI-driven credit scoring.
Learn what machine learning is, its types, and how it powers AI-driven credit scoring.
According to analysts' forecasts, by the end of 2024, the machine learning (ML) market size will exceed $79 billion. A significant portion of this – 15.4% – is attributed to the financial sector.
This trend is driven by the fact that machine learning algorithms can significantly optimize workflows in financial organizations, particularly by improving the efficiency of risk assessment.
The machine learning definition can be formulated as follows: it is a branch of artificial intelligence focused on creating algorithms and models that learn from the data they receive.
Machine learning systems are not explicitly programmed to perform tasks. They are structured in such a way that machines are learning from large amounts of data.
Based on this data, they make predictions and decisions.
There are several types of ML depending on how the machine learning model is trained:
1. Supervised learning. The model learns to predict outcomes and recognize patterns on labeled data. The desired result is known in advance, and the machine learns to find relationships between input and output data.
2. Unsupervised learning. The model works with unlabeled data. Its goal is to identify patterns without specific instructions on what to look for.
3. Reinforcement learning. The model learns through interaction with the environment. It receives feedback in the form of rewards and penalties and adjusts its strategies accordingly. It does not rely on direct instructions from a human user.
Artificial intelligence and machine learning are different, though related, concepts.
Artificial intelligence (AI) is a technology that involves a machine capable of mimicking human intelligence, while machine learning focuses on teaching a machine to perform a specific task by identifying patterns.
There are other differences between these concepts as well:
Modern fintech companies and lenders are increasingly turning to machine learning models to optimize various aspects of lending:
1. Credit risk management
2. Fraud detection and prevention
3. Credit scoring
AI-based systems allow for the use of alternative data for credit scoring. This includes social media activity, online transaction history, data on phone numbers, emails, and IP addresses.
By using AI in credit risk management, lenders can analyze information obtained from non-traditional sources, more accurately predicting the likelihood of default.
Machine learning algorithms demonstrate excellent results in combating fraud in real time.
With effective identity verification technologies and other methods, scoring systems can increasingly identify suspicious consumer behavior, distinguishing fraudulent actions from legitimate ones.
When comparing traditional vs alternative credit scoring, the latter has several advantages.
First, credit scoring using ML provides financial organizations with a comprehensive profile of potential borrowers. Unlike traditional scoring, it relies not only on historical financial data, which may be insufficient or outdated.
Second, automated credit decisioning using alternative data enhances financial inclusivity, as this approach opens access to lenders' services for unbanked individuals.
The scoring system methods at RiskSeal are built on AI machine learning. The platform uses advanced models and algorithms to create accurate and reliable credit ratings.
Here are the AI and ML technologies employed in RiskSeal.
1. Face recognition. This is the process of verifying an applicant's identity based on their photos.
The system compares images of the potential borrower found on social media, determining whether the same person is depicted in them. If the credit application algorithm requires a selfie, this photo can also be included in the comparison.
2. Name match. This technique allows for comparing the name provided by the applicant with other names listed by the user in public sources.
Any discrepancies can be considered indicators of fraud.
3. Anomaly detection. By using machine learning for high-risk applications, RiskSeal analyzes patterns and identifies atypical consumer behavior. This allows for the early suspicion of fraud during the credit application decision-making process.
The use of machine learning is a relevant credit scoring trend that defines the future of financial organizations. It enables lenders to reduce the likelihood of default, expand their target audience, and effectively combat fraud.
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