Press release: The (near) future of AI in financial analytics

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As AI continues to advance, we can expect to see even more innovative solutions, says Insight Consulting's Upuli de Abrew.

AI trends in finance data analytics
Artificial intelligence (AI) continues to transform various industries, including Finance. The progression of AI is exponential, and organisations are embracing it to drive business strategy, innovation and growth. Financial institutions are leveraging AI to improve their data analytics capabilities, enhance risk management and fraud detection, and drive operational efficiency.

There are many potential uses for AI in data analytics which can assist with small-scale projects, as well as large-scale enterprise initiatives. Recent advances in AI − such as Dall-E and ChatGPT − have shown the power of AI is truly incredible, and that its growth and learning is exponential, making it difficult to truly predict the long-term future of AI in data analytics. In the short to medium term however, as AI continues its inexorable advance, here are some of the trends that have emerged and are expected to gain further momentum within the finance industry.

AI automation will be the norm
As more businesses look to scale implementation of AI across their entire organisations quickly, it will be increasingly important for them to consider how they can automate the implementation of their machine learning algorithms to improve efficiency.

Machine learning algorithms are capable of learning from historical data and can identify patterns, relationships and correlations that humans might miss. Financial institutions are using these algorithms to make predictions about the performance of various financial products, analyze market trends, and evaluate credit risk.

While some aspects of data analytics can be automated, machine learning algorithms are currently notoriously difficult to automate given their dependence on carefully-crafted data sets that represent the broader population.

As a result, organisations looking to scale implementation of machine learning algorithms − at least in the medium-term − will require the expertise of data engineers to enhance high-quality data sets and algorithms manually.

The demand for data scientists will only increase as AI continues to be adopted at scale. Given the scarcity of experts in this field, organisations will likely need to increase wages and offer more competitive compensation packages to attract and retain data scientists.

Real-time data analytics
AI can use real-time data analytics to help companies make faster and better decisions, for example fraud detection. Many financial institutions are already using AI-powered systems to analyze large amounts of data in real-time to identify potential fraud. Systems are designed to monitor transactions and detect any unusual behaviour, such as large or unusual transactions, or transactions from unfamiliar locations. If the system identifies a potential fraud, it can alert the relevant authorities or automatically block the transaction, thereby preventing further damage. As the AI has greater and greater exposure and feedback, it will become more accurate at predicting fraud.

Another example of real-time data analytics in finance is algorithmic trading, which uses AI and machine learning algorithms to analyze market data in real-time and execute trades automatically. An algorithmic trading system might be programmed to identify patterns in stock prices, such as trending upwards or downwards, and then use this information to make buy or sell decisions. The system could also be designed to monitor news and social media to identify any events that could impact the stock market, and respond to these events in real-time.

These are just two examples of how real-time data analytics is being used in finance, but the possibilities are virtually endless. As AI continues to advance, we can expect to see even more innovative solutions that leverage real-time data analytics to drive business value in the finance industry.

Natural language processing advances
Natural Language Processing (NLP) is a field of computer science and artificial intelligence that focuses on the interactions between computers and humans using natural language. NLP enables computers to understand, interpret and generate human language, making it possible to analyze large amounts of unstructured data, such as news articles, social media posts and financial reports. Financial institutions will focus on using NLP to monitor and analyse market sentiment, track financial news, and identify potential opportunities or risks. The use of chatbots which are trained to understand natural language will continue to grow, allowing organisations to provide personalised and conversational experiences to customers, instantaneously and accurately.

Organisational culture shift
While some companies are already working with AI, many of them are likely still in the early stages of implementation.

As they look to implement AI at scale, they will need to prepare for an organisational change and culture shift. This includes carefully considering how to adapt their organisational structure − as AI will likely completely transform how data analytics is done, having the right structure in place will be crucial to take advantage of these benefits.

In summary, AI is rapidly changing the finance data analytics landscape, and the trends discussed above are merely the tip of an iceberg. As AI continues to evolve, financial organisations need to remain nimble and flexible in order to take leverage the capabilities of AI advances to drive innovation, improve customer experiences and enhance competitiveness.

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