Artificial Intelligence in Banking Risk Management
What Is AI?
Broadly speaking, artificial intelligence (AI) enables machines to think for themselves. For example, computers can ingest data—such as video footage, market trend information, or weather patterns—and analyze it via complex algorithms to spot trends and make predictions. AI can reveal insights that traditional statistical analysis cannot.
AI in Banking
AI’s ability to spot patterns and predict outcomes make it indispensable for risk management in the banking sector. AI risk management allows banks to better understand and mitigate risk more effectively.
AI technologies allow banks to assess a vast number of data points and quickly realize insights that help them protect against losses and boost ROI for their customers. Leveraging large, complex data sets, banks can develop risk models that are more accurate than those based on standard statistical analysis.
Real-Time Risk Management for Banking
New priorities such as counterparty risk mitigation, stress testing, and intelligent fraud detection require rapid responses. To counter these, banks are pursuing real-time risk management from their AI platform services—which requires optimized frameworks, libraries, and hardware accelerators for AI workloads.
Banking Risk Management Technology
Technologies commonly leveraged by banks for AI risk management include:
Machine learning uses parameters from known, existing data to predict the outcome of a similar set of data. To do so, it relies on a prescribed set of criteria that is considered important within the data set. For example, Proportunity uses ML to accurately forecast house prices and identify those with growing value based on criteria such as price history, property layout, postcode, surrounding businesses, and crime. The company uses these insights to offer equity loans against the future price and help first-time buyers invest in housing.
Deep learning is a type of machine learning that’s receiving increased focus in the banking sector. As opposed to machine learning, deep learning algorithms do not need to be told about important criteria within data sets. Instead, they discover features from data on their own using a neural network. Banks are using deep learning to solve extremely complex problems that are difficult to solve via machine learning.
Natural Language Processing
Natural language processing provides banking risk management tools with the ability to understand both verbal and written human communications—including intent and sentiment. Deep learning and machine learning tools are often applied to enhance natural processing capabilities.
Analytics and Big Data
While big data analytics do not necessarily require AI capabilities, they’re used in a similar fashion to help banks unlock insights and better understand their risks. Tools such as Hadoop have enabled banking IT departments to place analytics capabilities close to data sources, enabling faster insights.
Types of Risk in the Banking Sector
Across the banking industry, AI technologies are being applied to an ever-increasing number of risks.
Credit risk is based on the potential loss suffered when borrowers or counterparties fail to make payments on debts. Here, banks are using machine learning and natural language processing technologies to conduct more expansive and thorough probability-of-default analysis as well as enhance their detection of early warning signs.
As capital markets fluctuate, banks face considerable threats to their bottom line. To keep pace with fast-moving market factors, banks use AI tools such as machine learning, deep learning, and natural language processing to forecast trends and enhance decision-making. For example, a bank might use an AI tool to analyze massive amounts of social media activity and determine the current consumer sentiment about a publicly traded company—which can then be used to predict the corresponding market activity or investment strategy.
Operational risk refers to the risk of loss due to inadequate internal systems or processes, as well as loss from system breaches or service interruptions. To address this risk, machine learning algorithms can ingest massive amounts of data—including unstructured data like written risk reports—to help banks spot areas for improvement and identify where outside factors pose the most significant risk.
Banks use a variety of models to make predictions and plan their activities. But what happens when one of these guiding models is wrong? This risk is referred to as model risk. To mitigate it, banks use AI to monitor other ML and AI systems and identify algorithm bias, fairness, inaccuracy, and misuse.
In our increasingly connected world, banks face cybersecurity threats from more attack vectors than ever. To detect malicious activity and mitigate risks, they use machine learning and deep learning technologies to identify anomalies on IT systems and predict attacker behavior such as target choice or infiltration method.
Banks also face the risk of other economic effects harming their business—such as a financial crash in a foreign market influencing an existing loan agreement, or the global market impacts caused by the COVID-19 pandemic. Deep learning and machine technologies are applied here to help banks understand the potential impacts, spot warning signs from other banks, and determine the appropriate mitigation measures.
Regulatory compliance is a rigorous and complex process for banks. They constantly face the risk of legal sanctions, financial loss, or negative impacts on their reputations because of failure to comply with laws and standards. To mitigate this risk, many banks are looking to confidential computing technologies that help streamline compliance while dramatically improving the security of sensitive workloads and data. Banks also use AI technologies to detect compliance gaps and ensure adherence to guidelines.
Intel Banking Technology
Intel works with an extensive ecosystem of partners—including OEMs, ISVs, and OSVs—to help banks harness the business value of their AI investments and optimize AI workloads to run on Intel® architecture.
We’re constantly working to push the envelope alongside our industry partners. For example, alongside our software partner Matlogica, we’ve delivered a more than thousand-fold speed-up for xVA pricing calculations on Intel® Xeon® Scalable processors. We’ve also worked alongside Quantifi to accelerate derivative valuations by 700x using AI on Intel® processors.
We offer a range of tools that help streamline and accelerate AI software development, including significant investment in AI frameworks such as PyTorch and TensorFlow.
Intel® Xeon® Scalable processors provide built-in AI features such as Intel® Deep Learning Boost, an AI accelerator that helps banks rapidly extract insights from their data. We also offer the Intel® Advanced Vector Extensions 512 (Intel® AVX-512) instruction set, which increases the performance of complex computational workloads such as XVA pricing applications. Our upcoming Sapphire Rapids processors will offer even more AI advancements, including support for Advanced Matrix Extensions to accelerate matrix-heavy workloads such as machine learning.
Finally, we offer a portfolio of Intel® Optane™ storage and memory technologies that deliver the higher throughput and low latency required by AI workloads. This enables key partners such as KX and Hazelcast to address their customers’ needs for real-time risk management.