How to adopt AI in the financial industry?
Artificial intelligence plays a key role in the financial industry, enabling process automation, contributing to real-time analysis of complex data, and driving and evolving the way business insights are analyzed and created.
Among other contributions, it contributes to Improve decision-making accuracy, identify patterns and trends, manage risks and detect fraud.
It even offers the possibility of personalizing the customer experience, offering investment recommendations and optimizing various operations, such as portfolio management and the detection of trading opportunities.
In this article we analyze how to adopt AI in the financial industry, to achieve greater efficiency, security and adaptability, in an increasingly complex and dynamic sector.
Current state of artificial intelligence in the financial sector
A recent analysis of Jeff Kearns, Senior Communications Officer of the International Monetary Fund, highlights that Financial institutions could double their spending on artificial intelligence between 2024 and 2027.
In this regard, he refers to a market study by International Data Corp, which estimates that the financial sector's spending on this technology could more than double, to reach $97 billion in 2027, with a compound annual growth rate of 29%.
In his article, Kearns highlights that AI tools and those who use them are the two new Essential elements of financial institutions and central banks around the world.
Mentioning an application case, he mentions the experience of JPMorgan, which has an AI research group made up of around 200 employees.
A fact that shows the growing relevance of artificial intelligence in financial institutions.
For its part, Accenture estimates in its report 'Banking on AI', that Banks could increase their productivity by up to 30% over the next three years by applying generative AI.
He also believes that banking will be one of the sectors most deeply impacted by this technology.
Importance of adopting AI in the financial industry
The application of artificial intelligence in the financial industry is crucial, due to its ability to analyze large volumes of data quickly and accurately, identify patterns and trends, and Make decisions based on predictive analytics.
With AI, financial institutions can Improve the accuracy of your forecasts, manage risk, detect fraud more efficiently and customize your services.
In addition, artificial intelligence makes it possible to automate repetitive processes and reduce operating costs, promoting profitability and competitiveness.
It also provides the possibility of improve customer experience, by providing fast and personalized responses.
Benefits of implementing artificial intelligence in the financial sector
For Fernando Tormo, Commercial Director of IT PatagoniaThe benefits that AI brings to the financial industry are associated with the speed in creating relevant content for customers. In addition, the fact that it allows for 7×24 interaction between the financial services company and its users.
“In the near future, and based on comprehensive knowledge of digital banking users, Each customer can be reached with a product at the exact moment they need it.. This sensitivity in the model can define the future loyalty of a client," says the executive.
Another benefit that AI can bring to the financial industry is the automation of repetitive processes, such as data entry and document classification and management.
This automation frees up team time and capabilities for more strategic and higher-value delivery tasks.
By improving operational efficiency and streamlining decision-making, financial institutions are able to adapt more quickly to dynamic market conditions and the changing needs of their customers.
AI can also analyze large volumes of financial data quickly and accurately, identifying patterns, trends and correlations that can be difficult to detect using traditional methods.
Similarly, this technology has the ability to identify suspicious patterns or anomalies in financial transactions, helping to detect and prevent fraud more effectively.
Additionally, it can strengthen cybersecurity by identifying and mitigating potential vulnerabilities in information systems.
Large Language Model: the evolution of AI in the financial sector
The Large Language Model (LLM) is an artificial intelligence technology that uses deep neural networks to understand and generate text in a contextually relevant and coherent manner.
These models, like OpenAI's GPT-3, when trained on large amounts of textual data, can understand human language and generate responses based on the context provided.
Its impact on the financial sector is significant, as it allows for greater operational efficiency, a more informed decision making and better customer experiences.
In fact, LLMs are being used by financial institutions for a wide range of applications. From automating administrative tasks to analyzing complex financial data.
Among other capabilities, these models can assist in financial reporting, fraud detection, forecasting, and investment strategy development.
By analyzing large volumes of data in real time, LLMs can identify patterns and trends that might otherwise go unnoticed by humans, helping to manage risks and opportunities more effectively.
From IT Patagonia, as a regional strategic partner of the collaborative data science platform Dataiku, we are promoting a special product that allows companies to integrate with the LLM they are currently using (OpenIA, Meta, etc.).
Their goal is to generate new content based on the information they have available to them, in order to provide solutions in a more timely manner.
An effective solution to adopt AI in the financial sector
Dataiku is a platform that allows clients in the financial industry to have the centralized data management, regardless of whether that information is hosted in an ERP, Excel file, CRM, etc.
“The great value that Dataiku has is that adapts to all roles in the data area, “Since it is a truly collaborative solution,” highlights Fernando Tormo.
This is a platform that saves a lot of time due to its focus on the financial business.
Among other features, it allows you to use previously developed templates, such as models trained in credit card fraud or customer segmentation.
7 Rapid AI Implementation Solutions in the Financial Industry
The change in consumer behaviour that has characterised recent years is also reflected in their interactions with the financial sector.
The challenge of being able to respond to people's expectations and provide satisfactory customer experiences requires companies to be committed to innovation and to be increasingly efficient.
In response to this challenge, the solutions pay and play based on data analytics and artificial intelligence allow to accelerate the development, research and analysis of customer behavior.
We share five examples of the use of the data science platform, Dataiku, in financial institutions.
1) Smart customer segmentation
The key today is to understand user behavior and preferences, and based on that understanding, to be able to segment them appropriately.
Having this knowledge and the ability to translate it into effective actions, oriented to the needs of people and companies, you can Personalize financial services and make recommendations that enhance the customer experience.
Dataiku’s enterprise customer segmentation solution enables you to enrich your segmentation approach by combining machine learning and existing techniques to deepen product expertise and marketing effectiveness.
Additionally, it reveals customer segmentation trends over time, ensuring all teams effectively understand their mix of past, current, and potential future customers.
Instantly actionable insights enable marketers to instantly understand revenue share and product mix, among other capabilities.
All this through pre-designed panels for segment, level and cross-selling analysis.
2) Credit score
Improving credit dashboards with more powerful data sources and faster collaborative review is vital to retaining and expanding a customer base.
AutoML (automatic machine learning) allows accelerate the credit scoring process and implement higher-performing models. In this way, an accurate prediction of events can be achieved and potential risks can be determined.
Dataiku’s credit scoring solution provides a unified space where existing business knowledge, machine-assisted analysis (e.g., automated search of a large number of features and feature iterations for credit signals), and real-time collaboration with credit dashboards come together.
This is highlighted by Benjamín Libman, Content Marketing Manager at Dataiku, in his article '7 Solutions for AI in Financial Services', in which he mentions that Credit teams can immediately benefit from a machine learning-assisted approach.
In this way, a foundation is established on which to build dedicated credit scoring AI models, while maintaining the connection to the current customer base and systems.
3) Fraud prevention
Integrating machine learning into fraud detection optimizes the identification of fraudulent behavior, offering an opportunity to limit the impact of potential losses and improve customer confidence.
For credit card fraud detection, Dataiku provides a unified scoring space that uses business and machine learning rules.
Additionally, explore data with analytical information, quickly and comprehensively, and process API integration and alert management in real time.
4) Financial forecast
Generative AI makes it easier to create more accurate and less expensive forecasts, with immediate results.
5) Service coverage analysis
Spatial exploration of the distribution network is possible through the collection, processing and analysis of data from technologies such as GPS, location sensors and satellites. This allows Improve coverage strategies and competitive positioning.
6) Classification of anti-money laundering alerts
Improvements to anti-money laundering (AML) processes must occur at many points in the data chain.
Therefore, Benjamin Libman believes that a modular solution that can be easily incorporated into existing flows to process alerts more efficiently is an effective means to Improve detection rates and reduce alert fatigue.
With Dataiku’s AML Alert Triage enterprise solution, financial crime analysts can strengthen their initial assessments by prioritizing risk likelihood.
7) Document analysis
The ability to leverage unstructured data through document intelligence is critical to financial services management.
Dataiku's Interactive Document Intelligence solution for ESG automatically consolidates unstructured document data into a unified, searchable, and auto-categorized database.
It offers information accessible through a dashboard and uses a modular database of ESG (environment, society and governance) keywords.
Conclusion
With the widespread use of digitalization, data has become the most valuable asset for organizations.
Therefore, to be competitive, financial institutions need to clearly understand in which state they are operating, and from there, define business strategies based on accurate information.
Using advanced machine learning algorithms and natural language processing, AI can analyze large volumes of financial data efficiently and accurately.
This capability provides the ability to identify patterns, trends and correlations in data, which can be used to make informed decisions in areas such as investment management, risk assessment and fraud detection.
It also enables the automation of data collection, cleaning and analysis processes, which increases efficiency and reduces human error.
The implementation of AI in the financial industry optimizes data processing, providing a solid basis for strategic decision making and improving competitiveness. We invite you to learn about our Data Innovation Studio, to learn how to get the most out of the data generated by your organization, with cutting-edge technologies and tools.