AI transformation is happening faster than digital transformation did. Almost 75% of the world’s largest organisations have already integrated AI within their business strategies, says an Accenture survey on AI maturity. AI is rapidly being incorporated into their operations, with several pilot initiatives being scaled up and companies reporting better-than-expected outcomes. AI and ML technologies are transforming the banking sector and capital markets. This means industry players need to accelerate and upscale their offerings to unleash the full potential of the AI revolution.
Generative AI: A Brief Overview
In November 2022, OpenAI launched ChatGPT, a language model with as many as 175 billion parameters. ChatGPT is estimated to have reached 100 million monthly active users within two months of its launch. This fastest-growing consumer application in history is what is known as Generative AI, a tool that can understand instructions and generate content, images, music, videos, games, and much more. But this wasn’t the first GenAI application. The term GenAI was used for the 1966 launch of the ELIZA chatbot, created in the MIT Artificial Intelligence Laboratory. ELIZA attempted to mimic human interaction through pattern recognition but failed to understand context. This chatbot became useful only after the implementation of a deep learning technique known as generative adversarial network (GAN).
GenAI is powered by Natural Language Processing (NLP) and Natural Language Generation (NLG). While NLP deals with the processing of human language so that computers can understand and analyse it, NLG focuses on generating human-like text that is coherent and contextually relevant. Today, AI is rapidly progressing towards its ‘super-intelligence’ stage, where it is projected to be able to ‘replicate’ human thinking patterns.
What are the Limitations?
Two prominent challenges in AI adoption are:
- Bias in decision-making due to the use of limited datasets for training ML models.
- Privacy concerns under increasingly tightening regulatory oversight.
AI is also expensive to create and maintain. Its usefulness is based on learning, which is a continuous process to keep it “intelligent” enough to meet the latest requirements.
AI Adoption in the Financial Sector
With increasing dependence on digital platforms and tools for business operations and investment, the financial sector is adopting AI-enabled solutions with caution. The two key applications where AI adoption has been a gamechanger are:
Fraud Prevention
Generative models facilitate the detection and prevention of fraud by learning and analysing patterns and identifying anomalies. They assist humans by generating alerts and highlighting potential risks. The capabilities of Gen AI fraud detection have increased, as these smart machines have adaptive learning techniques to change their model with changing consumer needs and market insights. This proactive mechanism facilitates building a resilient and effective fraud management framework.
Credit Scoring
Generative AI has enhanced the accuracy, transparency, and efficiency of credit scoring. Customised credit risk assessment has enabled personalised credit offerings, credit portfolio optimisation, and feature engineering to meet customer requirements. This has not only enhanced customer experience, but also credit approval rates, while increasing transparency and trust in the credit decisioning process.
Further Applications
Other applications of AI in the finance industry are:
- Financial monitoring
- Investment predictions
- Process automation
- Secure transactions
- Risk management
- Advisory
- Customer data management
- Customer retention
- Marketing
With gaping holes in the application of data augmentation, virtual assistance, automation, personalisation, and predictive analytics, AI adoption has become one of the top agendas for the financial services industry.
AI and the Business of Trading
Artificial intelligence offers opportunities to brokers and large investment firms to improve their processes, enhance decision making and provide better customer support. The quantitative analysis capabilities of AI are already being used by these firms for diverse purposes:
- Market data generation, analysis, and signal generation by collating quantitative data, market and economic updates, sentiment analysis, and social media scanning.
- Algorithmic and high-frequency trading to assess market data in real-time, recognise signals, and make trading decisions based on a trader’s strategy, style and goals. These tools can automatically execute trades, reducing latency.
- Facilitate portfolio optimisation and risk management by assessing performance and risks under different market conditions with synthetically generated scenarios.
Bloomberg has applied Gen AI for diverse purposes, such as generating initial drafts for SEC filings and summarising financial content to reduce the cost of filing.
Disrupting the Future
As the future of finance progresses towards hyper-personalisation, significant advancements in AI will be required for fraud detection and prevention. Deeper credit analysis and intelligent decisioning will boost operational efficiency and transform customer experience, while improving risk assessment and mitigation capabilities.
As regulators across the globe strengthen AI scrutiny, the technology will be used both as an enabler and a monitor of compliance for complex regulations and the generation of insightful reports.
Goldman Sachs predicts that Gen AI will accelerate growth by 1.5% and lift global GDP by 7% between 2023 and 2032. In the finance sector, it is improving accuracy, efficiency, compliance, and customer experience, while reducing costs.
Acuity employs AI and NLP to bring together millions of news and data sets so you can empower your traders and investors with tradeable insights. Contact our team of experts now to learn more.
