Artificial Intelligence in Fintech: Use Cases and Examples (2024)

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.

AI is an extremely data-driven technology. Any system relying on it should be fueled with big data assets or streams of real-time information from several sources to deliver accurate analyses and forecasts or learn how to perform operational tasks.AI solutions include many interconnected elements (corporate systems, third-party databases, and IoT devices) which need to exchange multiple types of data but can use different communication protocols. Poorly integrated components would result in information silos, data inconsistencies, and inaccurate outputs.

Set up an ETL pipeline to integrate heterogeneous data from available sources and consolidate them into a NoSQL database, data lake, time series database, or other data storage. You can leverage cloud data integration services (AWS Glue, Azure Data Factory, etc.) or iPaaS platforms (such as Informatica) for this task.

Enable data exchange across the components of your solution by configuring suitable application program interfaces (APIs). Consider streamlining this process through cloud-based services such as Amazon API Gateway, Azure API Management, or Cloud Data Fusion API.

If the elements to integrate are not compatible in terms of communication protocols, you can set up an enterprise service bus (ESB) or other middleware architectures designed to convert them. Another option is to rely on data virtualization techniques.

Selecting the right algorithms to process your data can be a complex task given their sheer variety, ongoing research and debate about their performance in different scenarios, and financial organizations’ reluctance to share their experience to maintain a competitive advantage. Even with optimal datasets at their disposal, many financial companies can lack the technology infrastructure and, more specifically, the computing resources to process large data volumes or data streams in real time for adequate model training or analytics.

Algorithm selection should depend on the task to perform. Supervised learning algorithms like random forests, for instance, can be the best choice to predict future financial trends, while unsupervised learning algorithms like K-Means are a good option to segment customers and provide them with customized financial services.

Keep in mind that the best-performing algorithms, such as deep neural networks, require vast computing power and large training datasets but suffer from limited explainability due to their complex architectures. In a field like finance that requires maximum transparency, they should be chosen carefully and applied for selected tasks.

Major cloud service providers can integrate your in-house tech capabilities with tools and platforms, such as Amazon SageMaker or Azure Machine Learning, offering built-in algorithms, pre-trained AI models, and scalable computing resources.

When trained on low-quality or incomplete datasets, algorithms may not get a full view of key patterns and dependencies and are likely to generate an unreliable AI model providing inaccurate insights and predictions. According to PwC, lack of data is actually the greatest AI adoption blocker among financial organizations.

Given the black-box nature of AI (we don’t fully understand how algorithms come to a conclusion), models’ financial decisions can be less explainable and reliable than expected. In this regard, see the alleged gender bias of Goldman Sachs’ credit scoring algorithm. This problem is further amplified with deep learning and neural networks.

Two other common issues affecting AI models’ reliability are overfitting (the model was overtrained on a specific dataset and performs poorly with new data points) and model drift (its analytical and predictive capabilities degrade overtime due to progressive changes in input variables and their dependencies).

Map available sources to gather reliable, high-volume datasets. Depending on the use case, suitable sources can include market data providers (Bloomberg, Nasdaq, S&P Global, etc.), credit rating agencies, law enforcement or government watch lists, public records, or connected devices such as ATMs or POSs.

The professional community hasn’t yet fully understood AI’s decision-making mechanisms. However, AI engineers can mitigate this issue by identifying proper metrics to monitor algorithms' operation during training and after deployment and unveil potential reasons behind model bias or inaccuracies.

A common trick to minimize overfitting involves splitting the data processed during the modeling phase into training, validation, and test sets and cross-validating the outputs. You can also cut down the set of features considered by the model, selecting the most relevant metrics. This generally results in a more flexible model.

To address model drift, AIOps’ best practices recommend performing several retraining iterations in the post-deployment phase. This will enable ML experts to fine-tune the model with new data and adjust its output.

In a highly regulated industry like finance, with organizations and service providers managing sensitive information on a daily basis, the data-driven nature of AI can draw the attention of policymakers and raise concerns among the customer base.AI’s symbiotic relation with data also makes any solution relying on this technology a potential target for hacking and leaks.Multiple integrations with external sources and systems can further expand the range of vulnerability points exploited by fraudsters and cybercriminals.

Make sure to build and use your AI-based software in strict compliance with major data management standards and applicable legislation, such as GDPR, PCI-DSS, MISMO, and OWASP.

Whenever possible, use obfuscated data to train your AI models.

You can mitigate cyber risk exposure with various security features and techniques. These typically include encrypted data exchange, identity and access management based on a zero-trust approach, multi-factor authentication, user activity monitoring, dynamic data masking, and regular risk assessments via penetration testing.

Implement data governance policies and procedures defining how data should be handled and shared across your enterprise.

AI-enabled automation can trigger skepticism and fears of job disruption among your staff due to the fears of being replaced by software and bots.Many financial organizations can lack the in-house expertise to implement and take advantage of AI. At the same time, the job market suffers from a general lack of specialized talent, making recruitment more difficult.

Proactively invest in retraining and upskilling initiatives to help redeploy people to new positions and minimize job losses. Specifically, your organization should prioritize the development of technological, cognitive, and emotional skills.

Create external partnerships with experts in relevant fields to complement your staff’s expertise with no need to hire and train additional workforce. The rising adoption of remote work models and collaboration software can simplify this process.

Appoint the most skilled professionals among your teams to establish centers of excellence and supervise AI adoption across your organization.

Promote a corporate culture focusing on digital literature through workshops, incentives, and benefits.

Integrate your AI-based software with data visualization and self-service features to make it more user-friendly and help your staff familiarize with the product.