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👩💻 AI Use Cases in Finance and Banking
DTP #29: PLUS: Making the most of GenAI in 2024
Mukundan Sivaraj
December 15, 2023
Based on data from MarketResearch.Biz, the market size of GenAI in financial services achieved USD 847 million in 2022 and is anticipated to surpass USD 9.48 billion by 2032.
This growth has been fueled by major players such as IBM, Microsoft, and Amazon Web Services investing heavily into R&D to expand their Gen AI capabilities.
Looking downstream, we find that companies in financial services are still lagging, as they look to convert hype into tangible applications. (ex, in September, Japanese tech giant Fujitsu announced trials of GenAI for Japanese banks)
On the consumer side, however, the demand for accurate, and reliable financial services is clear. GenAI is poised to tackle those demands.
We look at projected financial and banking use cases for GenAI this week:
💼 AI in Business
GenAI's Impact in 2024: Making the Most of Opportunities
Image generated by Midjourney
A Salesforce survey indicates that 67% of IT executives have prioritized implementing GenAI over the upcoming 18 months.
Generative AI is poised to revolutionize businesses in 2024, offering both immense potential and challenges. An opinion piece from Entrepreneur offers a breakdown to harness its capabilities:
Competitive Edge of Early Adoption: Industry leaders investing heavily in generative AI gain a competitive advantage through:
Enhanced insights and operational efficiency.
Significant market advantages and cost savings.
Balancing Technology with Humanity: Businesses must integrate AI while valuing human expertise:
Reskilling efforts to balance technological integration.
Focusing on humane approaches amidst technological advancements.
Strategic Steps to Leverage Generative AI:
Resist 'Wait-and-See' Approach: Understand AI's transformative potential early.
Early adopters witness improved customer insights and cost-effective operations.
Start with identifying one high-cost area for efficient AI integration.
Understanding Opportunity Costs: Large-scale investments by tech giants indicate the potential for disruptive markets.
AI early adopters gaining market share and scalability advantage.
Not adopting AI could make companies vulnerable to those harnessing its potential.
Explore reskilling and identify areas where human talent is indispensable.
Embrace AI's potential while fostering a culture that values human contribution.
According to a financial industry survey by research firm Omdia
93% of respondents are looking to adopt AI in some form
Over half the respondents have already implemented AI within their organizations
GenAI adoption lags behind AI adoption, with 9% only having completely adopted text-based GenAI applications
The primary use cases that directly apply to the financial industry are:
⚖ Regulatory Compliance
Generative AI can simplify compliance reporting in the heavily regulated financial sphere, automating tasks like document verification, enhancing anti-money laundering (AML), and streamlining know your customer (KYC) practices.
Assisting with risk and compliance involves the utilization of GenAI to aid both initial and secondary operational roles in recognizing pertinent regulations and compliance necessities, facilitating the discovery of relevant guidelines.
Presently, the technology hasn't reached a stage where banks can completely delegate risk and compliance responsibilities with absolute confidence. However, it already offers considerable assistance and potential contributions in these areas.
⚙ Process Automation
Streamlining document-heavy financial processes, including applications, contracts, and account statements, generative AI efficiently automates data entry, reconciliation, and other repetitive tasks, bolstering operational efficiency.
For example, GenAI can significantly benefit Accounts Payable (AP) automation by extracting and processing invoice data, replacing time-consuming manual entry prone to errors. It efficiently identifies essential details even from unstructured invoices, using natural language processing and image recognition for accurate data interpretation.
These capabilities are extended to General Ledger (GL) coding, transforming it into an automated, error-free process. By learning from past transactions, AI-based systems allocate costs accurately, ensuring faster and more precise AP processing. The self-learning nature of AI allows continuous improvement, making it adaptable to evolving business environments and accounting practices.
🕵️♀️ Detection and Prevention of Fraud
Generative AI's core strength lies in pattern recognition, aiding in the real-time identification of irregular transactional patterns within the financial sector to thwart fraudulent activities.
By training on both genuine and synthetic data, AI models can detect irregular patterns signaling potentially fraudulent activities. This anomaly detection capability can discern unusual transactions, bolstering fraud prevention measures by pinpointing deviations from established norms. GenAI's adaptability to evolving fraud patterns provides a crucial edge, as it dynamically adjusts to new fraud tactics, steadily enhancing detection rates over time.
Through unsupervised learning methods like Variational Autoencoders (VAEs) or Generative Adversarial Networks (GANs), AI models learn patterns independently, lessening reliance on labor-intensive manual labeling processes for identifying fraudulent cases.
🤖 Enhanced Customer Experience
By employing generative AI-powered chatbots and virtual assistants, financial institutions can elevate customer service with 24/7 support, contextualized information, and reduced wait times, thus enhancing overall customer satisfaction.
GenAI can empower bank agents to instantly elevate service by offering personalized insights, synthesizing dynamic conversation summaries, and driving innovation through data analysis. It facilitates real-time, context-rich responses and provides guidance for complex transactions, as witnessed at Commonwealth Bank. Banks leverage this AI technology to augment customer service, investment advice, and accelerate commercial loan approvals, benefiting clients and fostering business growth.
🌐 AI Weekly News Roundup
Essential AI, co-founded by Ashish Vaswani and Niki Parmar, has emerged with $56.5 million in Series A funding, supported by major tech players like Google, Nvidia, and AMD. Leveraging their expertise in AI, they aim to develop LLM-driven products for automating workflows.
Citrusx secured a $4.5 million seed investment to develop software ensuring AI compliance amid increasing global regulations. They aim to accelerate the process of taking AI models to production, emphasizing trust, relevance with updated data, and adaptability to new AI approaches like ChatGPT.
MIT has released a series of policy papers on AI governance, aiming to aid U.S. policymakers in regulating and overseeing AI effectively. The framework suggests extending current regulatory approaches, ensuring AI alignment with existing rules, and exploring AI's societal benefits while mitigating potential harm.
💻 Platform Highlight
Cohere: OpenAI competitor focusing on large language models (LLMs) for enterprises.
Rephrase: GenAI Text-to-Video platform, recently acquired by Adobe.
Leonardo.AI: AI platform for creating multimedia collateral. Recently raised $31M in funding.
How do you deal with people wanting definite answers when statistics isn't deterministic? - A Reddit thread
“Here is why I am so doubtful about the "talk-to-your-data" use of AI.” A thread
🤖 Prompt of the week
Act as a data visualization expert and create a [type of plot] that shows the relationship between [variable1] and [variable2] in [dataset] See you next week,
Mukundan
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