Knowledge Graphs: Transforming Personalized Finance
Umesh Birajdar
Senior Data Scientist, AIML- ADM
Umesh Birajdar, Senior Data Scientist at Fractal, delves into the hyper-personalization possibilities created by combining knowledge graphs and machine learning.
A knowledge graph visually represents the relationships between real-world entities — objects, events, situations, or concepts. They are becoming increasingly powerful artificial intelligence (AI) tools; they help reduce the need for large, labeled data sets and make transfer learning and explainability easier, resulting in richer data that is easier to feed into machine learning (ML) algorithms. While powerful, knowledge graphs work best when integrated with ML to deliver deeper insights and a more personalized customer experience.
Value creation in finance through knowledge graphs and ML
Knowledge graphs and machine learning offer great potential for value creation within the finance industry thanks to their capacity to interpret complex, interrelated data. They proffer an efficient solution to the daunting task of managing immense volumes of data within financial organizations. The ability to quickly extract and structure information from millions of documents in, for example, the wealth management division enables the institution to perform internal audits and precisely understand what each document contains.
The power of knowledge graphs is emerging within the finance sector, where data sensitivity and regulations restrict marketing and customer service activities.
Merging machine vision with AI, knowledge graphs, and ML has enabled enterprises to spot patterns in extensive data sets.
Customer service and retention are becoming increasingly important in today’s business climate. To this end, this capability enables personalized customer experiences to deliver remarkable outcomes.
As more of our lives are transferred to — and governed by — technology, customers are beginning to demand more personalized interactions with the companies vying for their business. Traditional personalization involves mining browsing history and behavioral data to craft individualized experiences, but this is no longer enough. The answer is hyper-personalization, quickly becoming the key to rich, customized online experiences. However, this level of personalization requires additional data.
Hyper-personalization for High-Net-Worth Individuals (HNWIs)
Knowledge graph store and organize an individual’s preferences, behaviors, and demographics data, making it easily accessible for use in personalization. For example, businesses can generate tailored product or content recommendations that align with customers’ tastes by leveraging an HNWI customer’s web activity, such as browsing habits, purchase history, and social media interactions. They can also connect HNWI customers with relevant experts, advisors, or other HNWIs with similar Interests.
But we can take this one step further, too. Combining knowledge graphs with ML provides a more comprehensive and accurate representation of data on a granular level, enhancing customer satisfaction by addressing multiple pain points and personalization challenges:
Marketing
ML can understand HNWIs’ behavior and preferences to create more effective marketing campaigns. By representing the relationships between different marketing campaigns and customer behavior in a knowledge graph, ML algorithms can make more accurate predictions.
Fraud Detection
By representing the relationships between different fraudulent activities, a knowledge graph can aid in detecting and preventing fraud through ML algorithms to identify patterns.
Risk Management
ML can help identify potential risks and develop effective mitigation strategies. A knowledge graph can represent the relationships between risks and mitigation strategies, enabling ML algorithms to make better-informed decisions.
Personalized Experiences
To meet the distinct needs and preferences of HNWIs, ML can personalize product and service offerings. A knowledge graph can map the relationships between products and services, enabling ML algorithms to offer more precise recommendations.
Personalized Wealth Management
ML can analyze HNWIs’ investment portfolios, predict market trends, and make informed financial decisions, utilizing knowledge graphs to represent relationships between different investments and market trends for more accurate results.
Data and Tech Requirements
As knowledge graphs represent interconnected data, the data sources it relies on are the primary driver of their creation. A technology like natural language processing (NLP) is then required to pre-process the data – i.e., extract the relevant data from the various collected sources. Using an advanced NLP built on a transformer model yields excellent results.
A well-designed ontology is required to build a graph database. From there, the necessary query and analysis can be done in the correct query language using built-in tools on the platform. Finally, visualization facilitates the end user to make sense of the data.
Any organization applying knowledge graphs in this context will also require cloud computing to manage all necessary resources.
Fig1: Outline for Extracting Knowledge from Data
Challenges
Unlike a relational database that stores information in tables, a knowledge graph is more intuitive and interconnected. Data extraction is one of the central challenges of creating and using knowledge graphs. It is a particularly tough challenge for unstructured data, requiring many pre-processing techniques, such as natural language processing and machine vision, before extracting meaningful information. In addition, creating a knowledge repository that accurately represents the relationships between different entities can be an overwhelming task, especially for large and complex data sets.
It is also critical to ensure the accuracy of the data and the entities extracted from it. Various metrics for evaluating NLP accuracy come in handy in verifying the correctness of the data or entities extracted. Additionally, graph neural networks can accurately identify and correct errors, optimizing the model’s performance by finding the shortest graph path.
Finally, the context and use cases of knowledge graphs are also important considerations. Knowledge graphs work best for interconnected or hierarchical data structures and may be less effective for other data types. However, they have various applications, such as search engines and recommendations, and can be especially useful for organizations seeking to create a central knowledge repository.
Fractal and the Future of Knowledge Graphs
Fractal is deploying AI and ML technologies to automate tasks like data cleaning, feature selection, and model building, focusing on reducing the need for manual processing and analysis. However, one of the biggest challenges in AI and ML is model explainability, especially as models become more complex.
Transparency and accountability are driving forces in finance, making explainable analytics solutions essential. Hence, we are focusing on developing solutions providing lucid and intelligible explanations for each model designed to deliver predictions or recommendations. By automating processes and explaining our models, we aim to improve the quality of our client data sets and extract more valuable insights from them.
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