The financial technology industry tends to be quick to incorporate state-of-the-art solutions. However, there is one technology that has come to center stage in recent years, and this is generative AI. Generative AI can do all of the above that conventional AI can do, plus generate new content, synthesize language, simulate data, and write code, where conventional AI could not. This presents revolutionary capabilities to FinTech companies seeking to enhance efficiency, customer happiness, and competitive advantages.
Now, it is time to investigate how FinTech can be transformed using generative AI and learn what the most exciting applications of AI will be and the best practices on how to put it to good use.
What is the Difference between AI and Generative AI?
Generative AI models that can create new data based on the patterns discovered in the existing data. In FinTech, this can refer to the creation of synthetic transaction-based data to test with, writing semi-personalized messages, generating reports, if you assess how to build an investment app, or even identifying fraud, as simulations of fraudster actions may be performed.
Classification or prediction was mostly what traditional AI was about. Generative AI goes further, and provides companies with the ability to automate the process of content creation and idea generation, but still do it at such a high quality as it would be done by people.
Use Cases of Generative AI in FinTech
Some of the most prominent use cases include:
Individualized Customer Message
Banks and FinTech apps can use generative AI to personally drive the composition of emails and in-app messages, as well as conversation exchanges driven by chat apps to each customer, personalizing with regard to behavior, spending patterns, and preferences.
A neobank may send a customer such a message as:
Jamie, we saw that you spent $250 on travel in the previous month. This is a personal savings plan to enable you to save up the amount of your next trip, i.e., 50 dollars per month.”
Such hyper-personalized nudges are proven to drive more engagement and conversion without exhausting marketing teams.
Generation of Reports and Summarization
Each day, financial analysts dedicate time to summarize, format, and proofread client-facing reports. Within a few seconds, generative AI can compose these reports based on the information found in internal systems and resources available online, and it is believed that generative AI tools will eventually be able to complete most of these tasks.
Portfolio summaries can be automated and include written, natural-language explanations of performance, risk risks and recommendations that a wealth management firm can deliver via quarterly portfolios. Instead of having to start over again, advisors just check and then perfect these drafts.
Fraud Detection and Threat Simulation
Whereas in traditional fraud detection, it is based on the ability to identify patterns in the data that have already been recorded, generative AI will allow us to recreate a fraud situation, using it to put fraud detection systems to the test. By generating synthetic transactions that are fraudulent, the firms will be able to grow their training data without violating any privacy regulations.
This enhances the resilience of the model, and it is more difficult to take advantage of blind spots by real fraudsters.
Model Training using Synthetic Data
Good quality data is imperative to the process of building AI in FinTech; however, privacy laws restrict the sharing and utilization of actual transaction histories. Generative AI has the ability to generate synthetic datasets with statistical equivalency of real datasets and can delete personally identifiable information.
Startups working on new credit scoring algorithms, say, can practice on large databases of simulated populations of millions of realistic customer profiles, without invading privacy and in some applications reaching higher accuracy.
Testing and Designing of Financial Products
Consider the case of a credit card company that would like to pilot a rewards program. Generative AI is able to take the responses of customers to different features, and thus, fast prototyping of financial products can be done without surveyors, which will take more time and are expensive.
Product teams can use more iterations to improve offerings before release, which can be done at an accelerated pace with AI-created customer personas and behavior simulations.
Steps to Implement Generative AI in FinTech
Let’s discover the main steps of implementing generative AI in FinTech:
Find Clear Use Cases
It is not about the tech, it is about the problem. Find business problems where generative AI may present a quantifiable business value. It could be saving on customer service, faster launch of products, more precise accuracy in detecting fraud and much more, but with a clear understanding of what is needed, the rest of the process is directed.
Data Quality and Privacy
Generative AI can only learn what is good in the data it learns. Take care in collecting, cleaning and organizing your data. When dealing with sensitive information, such as transactions or PII, look at the techniques of differential privacy or artificial data to prevent the violation of regulations.
Data providers who deal in compliant and anonymized datasets are also a popular option to jumpstart training at many firms.
Pick the Proper Models
All these are different in strengths: large language models such as GPT, image generators, or tabular-data-specific synthesizers. LLMs are effective on text-intensive assignments (chatbots, reports). Tabular data generators are preferable in the case of fraud simulations.
A popular FinTech strategy is also a hybrid model where open-source models and commercial APIs are impossible to separate due to cost and flexibility.
Combine with the Current Systems
Generative AI is not a place in itself- it ought to be exposed to your CRM, data warehouse, fraud tools, or developer background. Create APIs and pipelines to input the output of AI into business processes.
An example can be a generative AI system writing individual messages to customers, pulling customer information in your CRM, and pushing the final content to your marketing automation system.
Measure and Improve
Profile KPIs. Did AI make reports save hours for an analyst? Did the conversion rates improve with target messages? Use the data to tune the models, to retrain models with the newly collected data, and to scale up to new use cases.
The FinTech companies which actively view generative AI as an ongoing optimization process and not as a specific form of deployment get the most out of it.
Advantages of generative AI in FinTech
- Improved Efficiency: Automation of routine portions like report drafting or writing of codes allows teams to work on more valuable things.
- Better Customer Experience: Loyalty and churn are reduced with personalized, human interactions.
- Faster Innovation: Synthetic data is faster in customer-behavior simulation and provides quick results regarding hypothesis testing on products.
- Cost Savings: Operation costs are saved through reduced manual workloads and queries by the call centers.
- Improved Security: Multiple fraud mimicking and refinement of the detection model boost the level of security without compromising data confidentiality.
Difficulties with Gen AI Implementation
- Bias and Fairness: When training data is biased, then any trained machine model may also become biased, such as with credit scoring. Attention to auditing and testing is important.
- Regulatory Compliance: Financial services are also highly regulated. Outputs of generative activities have to satisfy privacy, security and transparency requirements.
- Trust and Explainability: Customers and regulators will require more clarity on the process of AI decision-making. Advanced generative models may be difficult to interpret.
- Cost and Complexity: Not very cheap to train large models or purchase API access. Firms should make sure ROI will pay dividends on the investment
Author’s bio
Yuliya Melnik is a technical writer at Cleveroad, a web and mobile application development company. She is passionate about innovative technologies that make the world a better place and loves creating content that evokes vivid emotions.