Jason Hurwitz, Sales Director, Europe
The power of AI in credit scoring
AI is revolutionising risk assessment in lending. According to recent analysis, it will save more than $1 trillion in the banking sector by 2030 by enabling lenders to assess risk in real-time, allowing for faster decision-making and greater efficiency.
One of the key ways it will unlock these efficiencies is via enhanced data analysis. AI algorithms can process vast amounts of data from diverse sources, including traditional credit bureau data, alternative data sets and even unstructured data, uncovering hidden patterns and correlations that may not be apparent to human analysis. By leveraging machine-learning techniques, AI models can identify complex patterns and correlations within data, enabling more accurate risk assessment. Furthermore, AI has the ability to continuously learn and adapt to changing market conditions, ensuring that credit scoring models remain relevant and effective over time.
Traditional credit scoring relies primarily on limited historical credit data, which means it may overlook relevant factors and rule out potentially creditworthy people due to a scarcity of historical financial data. This can lead to potential inaccuracies in credit assessments. AI algorithms are inherently predictive – meaning they can learn from past data to make predictions about future outcomes. In the context of lending, AI-powered risk assessment models can more accurately predict the likelihood of default or delinquency based on a borrower’s credit profile. By incorporating a wider range of data points and leveraging advanced machine-learning techniques, it can identify subtle patterns and trends that may signal increased credit risk, helping lenders make more informed decisions.
The inner workings of AI-driven credit scoring
At the heart of AI-driven credit scoring is the utilisation of machine-learning models. These models undergo training on extensive datasets to detect patterns and factors that could suggest a borrower’s probability of loan repayment. Through analysis of thousands of data points, machine-learning algorithms can unveil associations between seemingly disparate factors and an individual’s financial dependability.
After the completion of training, the machine-learning model can be utilised for predictive analytics. The model can make forecasts on new, unseen data. Upon receiving a new credit application, the AI system can scrutinise the applicant’s data against learned patterns from training and subsequently produce a score that more accurately forecasts the applicant’s creditworthiness.
What makes AI-driven credit scoring even more reliable is its capacity for continual learning and adaptation. With each new application processed and feedback received on its predictions, the system refines its models to enhance accuracy. This ongoing learning process ensures that lenders stay current with evolving financial trends and market dynamics.
Enhancing financial inclusion
Over 1.4 billion people worldwide do not possess access to banking services. One of the most promising aspects of AI-powered credit scoring is its potential to enhance financial inclusion by expanding access to credit for underserved populations. Traditional credit scoring models often disadvantage individuals with limited credit histories or those who lack access to traditional banking services. This can perpetuate cycles of poverty and restrict economic opportunities.
AI algorithms, however, can analyse alternative data sources – rental payments, utility bills and even educational background – to provide a more accurate assessment of risk for individuals who may have been overlooked by traditional credit scoring methods, thereby promoting financial inclusion and economic empowerment.
Moreover, AI can help identify opportunities to extend credit to underserved populations in a responsible manner. By leveraging predictive analytics, lenders can identify creditworthy individuals who may have been overlooked by traditional scoring models, thereby expanding access to credit for those who need it most.
AI credit scoring outperforms traditional models
The decision-making process for conventional credit scoring is rule-based, subject to human judgement and bias with longer processing periods, while AI-driven scoring offers swift and efficient evaluations with more objective assessments, minimising or eradicating human bias. AI systems have the ability to process applications and generate scores at a much faster pace compared to traditional methods, thus accelerating the lending process.
In today’s increasingly fast-paced world of finance, risk assessment stands as a cornerstone of responsible lending practices. Traditionally, lenders have relied on historical data and manual analysis to evaluate borrowers’ creditworthiness – but automating credit scoring and decisioning can streamline operations, substantially reduce costs and improve customer experience. This increased efficiency benefits both lenders and borrowers, facilitating faster access to credit while minimising the risk of default.
