Knowledge Graphs have emerged as a compelling abstraction for capturing key relationship among the entities of interest to enterprises and for integrating data from heterogeneous sources. JPMorgan Chase (JPMC) is leading this trend by leveraging knowledge graphs across the organization for multiple mission critical applications such as risk assessment, fraud detection, investment advice, etc. A core problem in leveraging a knowledge graph is to link mentions (e.g., company names) that are encountered in textual sources to entities in the knowledge graph. Although several techniques exist for entity linking, they are tuned for entities that exist in Wikipedia, and fail to generalize for the entities that are of interest to an enterprise. In this paper, we propose a novel end-to-end neural entity linking model (JEL) that uses minimal context information and a margin loss to generate entity embeddings, and a Wide & Deep Learning model to match character and semantic information respectively. We show that JEL achieves the state-of-the-art performance to link mentions of company names in financial news with entities in our knowledge graph. We report on our efforts to deploy this model in the company-wide system to generate alerts in response to financial news. The methodology used for JEL is directly applicable and usable by other enterprises who need entity linking solutions for data that are unique to their respective situations.

Large language models (LLMs) have demonstrated outstanding performance across various tasks, yet they still exhibit limitations such as hallucination, unfaithful reasoning, and toxic content. One potential approach to mitigate these issues is learning from human or external feedback (e.g. tools). In this paper, we introduce an intrinsic self-correct reasoning framework for LLMs that eliminates the need for human feedback, external tools, and handcraft prompts. The proposed framework, based on a multi-step reasoning paradigm \textbf{Le}arning from \textbf{Co}rrectness (\textsc{LeCo}), improves reasoning performance without needing to learn from errors. This paradigm prioritizes learning from correct reasoning steps, and a unique method to measure confidence for each reasoning step based on generation logits. Experimental results across various multi-step reasoning tasks demonstrate the effectiveness of the framework in improving reasoning performance with reduced token consumption.

With more than 5,000 branches across 48 states and 80 million customers, each with its own unique requirements to satisfy its customers' financial needs, a clear data strategy is key for JPMorgan Chase. According to Mark Birkhead, firm-wide chief data officer at JPMorgan Chase, data analytics is the oxygen that breathes life into the firm to deliver growth and improve the customer experience. Providing first-class business in a first-class way for clients and customers applies to every part of the firm, including its heavy investments in data analytics, machine learning, and AI. Using these advanced technologies, JPMorgan Chase can gain a deeper understanding of the breadth and specificity of the needs of the customers and communities it serves. "It means using our data to drive positive outcomes for our customers and our clients and our business partners. And it means using this to actually help our customers and clients manage their daily lives in a better, simpler way," says Birkhead.

"We coach our teams that success and innovation does not come from rebuilding something that somebody has already built, but instead from leveraging it and taking the next leap with additional features upon it to create high impact business outcomes," says Menon. At JPMorgan Chase, technologists are encouraged, where possible, to see the bigger picture and solve for the larger pattern rather than just the singular problem at hand. To reduce redundancies and automate tasks, Menon and her team focus on data and measurements that indicate where emerging technologies like AI and machine learning could enhance processes like onboarding or transaction processing at scale. AI/ML have become commonplace across many industries with private banking being no exception, says Menon. At a base level, AI/ML can extract data from documents, classify information, analyze data smartly and detect issues and outliers across a wide range of use cases.

Enterprises need to constantly look for ways to improve and expand what they offer to the marketplace. For example, Sameena Shah, managing director of AI research at JPMorgan Chase, says the company's bankers have been looking for new ways to study early-stage startups looking to raise capital. The challenge was, she says, "finding good prospects in a domain that is fundamentally very opaque and has a lot of variability." The solution for JPMorgan Chase was a new digital platform, built off an algorithm that continually seeks out data, and learns to find prospects by triaging its data into standardized representations to describe startups and likely investors. For users, the platform also offers the context of its output, to help them understand the recommendations.

These best governance practices involve "establishing the right policies and procedures and controls for the development, testing, deployment and ongoing monitoring of AI models so that it ensures the models are developed in compliance with regulatory and ethical standards," says JPMorgan Chase managing director and general manager of ModelOps, AI and ML Lifecycle Management and Governance, Stephanie Zhang. Because AI models are driven by data and environment changes, says Zhang, continuous compliance is necessary to ensure that AI deployments meet regulatory requirements and establish clear ownership and accountability. Amidst these vigilant governance efforts to safeguard AI and ML, enterprises can encourage innovation by creating well-defined metrics to monitor AI models, employing widespread education, encouraging all stakeholders' involvement in AI/ML development, and building integrated systems. "The key is to establish a culture of responsibility and accountability so that everyone involved in the process understands the importance of this responsible behavior in producing AI solutions and be held accountable for their actions," says Zhang. This episode of Business Lab is produced in association with JPMorgan Chase.

A number of datasets for Relation Extraction (RE) have been created The exponential progress of AI across multiple domains can largely to aide downstream tasks such as information retrieval, semantic be attributed to the availability of large datasets coupled with an search, question answering and textual entailment. However, increase in available compute power. Relation extraction (RE) from these datasets fail to capture financial-domain specific challenges text is a fundamental problem in NLP and information retrieval, since most of these datasets are compiled using general knowledge which facilitates various tasks like knowledge graph construction, sources, hindering real-life progress and adoption within the financial question answering and semantic search. It has seen significant world. To address this limitation, we propose REFinD, the progress in recent years, thanks to advanced machine learning techniques first large-scale annotated dataset of relations, with 29K instances and the availability of large-scale relation extraction datasets.

The hype around artificial intelligence (AI) has skyrocketed since the launch of ChatGPT, the chatbot from OpenAI. In just two months, ChatGPT was estimated to have reached 100 million monthly active users, with wide-ranging use cases including writing essays, debugging code and composing music. Such a leap in functionality and adoption prompted leading lights in the technology industry to call for a'pause' in the development of powerful AI systems. On March 22, the non-profit organisation Future of Life Institute published an open letter urging AI research facilities to put a stop to the creation of systems that can match human intelligence. More than 50,000 industry figures -- including CEO of SpaceX, Tesla and Twitter Elon Musk; Apple co-founder Steve Wozniak; and Chris Larsen, co-founder of Ripple -- have added their signatures to halt the training of models larger than GPT-4, the newest version of OpenAI's language model system.

DeepSee.ai, the creator and leading provider of Knowledge Process Automation (KPA), announced that it has been inducted into the JPMorgan Chase Hall of Innovation. The bank's Hall of Innovation award recognizes select emerging tech companies for their innovation, business value, and disruptive nature. "We're extremely honored to accept this award from JPMorgan Chase" "DeepSee has helped us automate manual post-trade checks supporting complex derivatives trading into AI-powered business outcomes," said Tom Damico, Global Head of Equities Operations, JPMorgan Chase. "We're already seeing efficiencies in post-trade processing and reconciliations, with more efficient deal review timeframes and more importantly, reduced operational risk." "We're extremely honored to accept this award from JPMorgan Chase," said Steve Shillingford, CEO of DeepSee.

Executives across industries are turning to automation to deliver on cost optimization and enhanced productivity objectives, Saikat Ray, VP analyst at Gartner, told CIO Dive in August. The robotic process automation software market will reach $2.9 billion by the end of 2022, up 19.5% from 2021, according to Gartner. In recent years, large UiPath bank customers have been using automation tools to facilitate initiatives that include data extraction and data transfer efforts to support the merger of BB&T and SunTrust; reduction of manual work for Wells Fargo contact center agents through digital personal assistants; and the delegation of some structured, rule-based repetitive tasks to bots at JPMorgan Chase. From JPMorgan Chase's perspective, one of the next steps on its automation journey will include using bots to tackle more sophisticated tasks, including delving into unstructured processes and unstructured data, and using machine learning to facilitate these efforts, said Shefali Shah, managing director of global digital transformation and integrated intelligent automation at JPMorgan Chase. Diana Caplinger, executive vice president and head of enterprise enablement and intelligent automation at Truist, said the company is deploying automation in support of "integrated relationship management," an effort to use data across the organization to deliver more personalized service to clients.