AI in fintech: use cases,

solutions & implementation challenges

AI in fintech: use cases,
 solutions & implementation challenges

December 20, 2023

AI in the fintech industry: current state & the future

the estimated size of the global AI in fintech market in 2023

Mordor Intelligence

of financial companies believe AI will benefit their business in the next 3 years

The Economist

the additional value AI can generate in the global banking industry annually

McKinsey

Top use cases of AI in fintech

Financial organizations and fintech service providers can implement AI in a wide range of corporate functions and software solutions or to address multiple business challenges.

Scheme title: Current and planned AI adoption across financial use cases
Data source: The Economist — Banking on a game-changer: AI in financial services

We use it heavily

We currently do not use it, but we plan to in the next 1-3 years

Fraud detection

Compared to traditional systems based on strict sets of encoded rules, ML-powered fraud detection solutions recognize a wider range of anomalies that may be signs of fraud, ensure a lower rate of false negatives, and can learn to detect new threats. This results in faster, more accurate, and cost-efficient fraud prevention operations.

  • Prevent market manipulation, such as spoofing and wash trading, by monitoring and cross-checking brokers’ activity to spot anomalies or inconsistencies.
  • Fight money laundering by tracking anomalous transactions, including large sums exchanged by newly established corporations registered in tax havens.
  • Identify credit card fraud by spotting anomalous account behavior and purchase patterns, such as multiple payment methods added in a short timeframe.

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Examples of AI-powered fintech tools & services

TradeSmith's flagship product is an investment portfolio management platform that leverages AI algorithms to forecast the price of different S&P 500 stocks and help users optimize trade order execution timing via automation. The company partnered with Itransition to equip the solution with several key features ranging from real-time stock data monitoring to risk management alerts.

Dashboard

Image title: Dashboard

AI-related technologies applied in fintech

Artificial intelligence is a vast realm with multiple sub-branches that can be combined to craft fintech solutions tailored to specific fields of application.

ML systems use supervised and unsupervised learning algorithms to process large datasets and autonomously learn how to complete analytical or operational tasks without being explicitly programmed to do so. Applications of ML include:

Data mining

to sift through big data and identify patterns, anomalies, or relationships among variables
Example of use

Identifying outliers for fraud detection

to acquire and process visual data for object detection, OCR, or image analysis

Example of use

Validating transactions via face recognition

Natural language processing

to derive insights from texts and interact with humans via conversational AI
Example of use

Powering robo-advisors in fintech apps

Common challenges of implementing AI in fintech

As pointed out by The Economist, AI implementation in the financial sector can be trickier than expected due to a combination of business and technical complexities. Here are some guidelines and best practices to overcome the most common adoption roadblocks.

Scheme title: Adoption barriers and risks of adopting AI in financial services
Data source: The Economist — Banking on a game-changer: AI in financial services

In your view, what are the most prominent barriers to adopting and incorporating AI technologies in your organisation? Select up to three.

In your view, what are the greatest risks to your organisation associated with AI adoption? Select up to two.

ROI & management buy-in

AI-powered solutions are typically more challenging, time-consuming, and financially demanding to implement than conventional software due to their complex architecture, long training process, and remarkable computing requirements. The potential cost of an AI implementation project can make it more difficult to achieve ROI and secure stakeholder and executive buy-in. Indeed, a study by PwC highlighted that budget constraints rank second among the top barriers to AI adoption in the financial sector.

Involve expert AI consultants from your project’s early assessment stage to select between AI-powered and conventional solutions based on your budget, existing tech ecosystem, and the business challenges you need to address.

To ensure AI adoption is worth the effort and investment, prioritize the most important and profitable corporate functions depending on your business scenario, or target those suffering from severe inefficiencies that can’t be solved with “traditional” technologies. 

Keep an eye on early adopters’ initiatives and AI trends in the financial industry. According to The Economist, for instance, the most popular AI use cases in finance currently include fraud detection, IT operations, digital marketing, risk assessment, customer experience personalization, and credit scoring.

Gaining an edge with AI-driven fintech

Gaining an edge with AI-driven fintech

Challenged by fierce competition, complex market dynamics, and customer demand for a personalized user experience, financial players have pinned their hopes on AI and digital transformation, achieving promising results in every business domain. That said, companies shouldn’t approach AI as a purely “plug-and-play” tool, since its successful implementation calls for expertise, ongoing human supervision, and a robust, interconnected tech ecosystem. Consider relying on Itransition’s experience in AI-related projects to meet these requirements and facilitate adoption across your organization.

Gaining an edge with AI-driven fintech

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FAQ

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