AI in fintech: use cases,
 solutions & implementation challenges

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.

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.

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.

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.

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.