Artificial Intelligence and Risk Management

Relevant data, useful model, efficient AI engine

Artificial Intelligence (AI) software can evaluate unstructured data about risky behaviours or any activity in the corporate operations. AI algorithms can identify pattern of behaviours related to past incidents and turn them into risk predictors. AI tools can also classify structured and unstructured data according to previously defined patterns and categories; access to this information can be monitored and controlled.

AI ability to spot patterns and predict outcomes makes it indispensable for risk management in a financial organisation; ultimately, it leads to better risk mitigation. Using large and complex data sets, companies can develop risk models that are more accurate than those based on statistical analysis. AI platforms allow rapid responses to changes in risk scenarios and stress testing. Risk Management applications usually include one or more of these technologies:

 

  1. Machine Learning uses parameters from known, existing data to predict the outcome of similar set of data relying on criteria that are considered important within the data set.
  2. Deep Learning discovers features from data without using any predetermined criteria but uses a neural network. It is used to solve complex problems that are too difficult to solve using machine learning.
  3. Natural Language Processing enables banking risk management tools to understand verbal and written human communications. Deep learning and machine learning tools are often applied to enhance natural processing capabilities.
  4. Big Data Analytics do not necessarily require AI capabilities. As we discussed in a separate white paper, they are often used to gain a better understanding of risk patterns.

 

Standard Risk Analysis

Standard risk analysis can be taken to another level using those technology. For instance:

 

  • Credit Risk – Machine Learning and Natural Language Processing are used to increase detection of early warning signs of default and conduct probability of default analysis.
  • Market Risk – Machine learning, deep learning, and natural language processing are used to forecast trends and enhance decision-making. For instance, AI could look into social media activity to determine consumers’ attitude towards a specific public traded company and use that information to predict market activity, investment strategy, or future trends in the value of the company’s financial assets such as shares, corporate bonds, and their derivatives.
  • Operational Risk – Machine Learning can be used to process very large amount of structured and unstructured data to help organisations spot areas for improvement and identify outside threats to operations.
  • Model Risk – AI technology, in general, can be used to analyse risk models and identify algorithm bias, fairness, inaccuracy, and possible misuse
  • Cybersecurity Risk – AI technologies can also be used to identify anomalies in the corporate IT landscape, identify possible vulnerabilities, predict attacker behaviour such as target choice or infiltration method.
  • Contagion Risk – Deep learning and Machine Learning can be used to understand the potential impact on their business of economic event happening worldwide. They can help spot warning signs from other banks – whether domestic or international – and determine appropriate mitigation measures.
  • Compliance Risk – AI technologies can be used to detect compliance gaps and ensure compliance with guidelines and rules. They can also help streamline compliance and improve the security of sensitive workflows and data.

 

AI technology can also be used for more complex risk and market analysis. Such as trend analysis or conditional probability. For instance, let us assume that movement in the price of stock A caused movement in the value of Stock B and C. Market players reacting to that may create movement in the price of stock D and E to protect their positions in stock F. Conditional probability can be used to assess whether movement in D and E were prompted by the movement in A and the reactions of B and C; or was a natural movement as result of fundamental economic factors. The overall aim is to support trading decisions in any or those stocks, for instance deciding whether to trade any of those shares, divest or simply wait for prices in those stocks to stabilise.

 

How ALFO SABR can help

ALFO SABR has been designed to support conditional probability analysis and trend analysis. Discover more about ALFO SABR

 

About ALFO

ALFO is a Deeptech IT company that excels in using AI driven solutions to deliver multi asset risk management, trading, and investment performance tools to the professional financial services sector. Our clients enjoy pain free access to systems and AI technology they previously thought unattainable and beyond their reach. We make you better, faster. We allow you to do things you thought you could not do.

 

The ALFO advantage:

• Custom-tailored technology that adapts fluidly to your ecosystem
• Quality and performance essential to compete at any level
• Agility and dynamism to embrace change
• Significant Reduction of Total Cost of Technology Ownership

 

ALFO SABR is a multi-asset, multi-currency and multi-market real-time risk management and hedging system supporting exchange-traded and OTC instruments. Discover more about ALFO SABR