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Financial institutions struggle with inefficient post-trade processing
Financial institutions waste substantial time and resources processing complex financial documents. These documents are critical, as they govern trillions of dollars in transactions, yet the process is bogged down by legacy systems and manual workflows. This inefficiency leads to significant financial losses, with hedge funds losing millions annually due to outdated processes, and the industry as a whole paying over $914 billion in settlement failures over the past decade.
Legal and operations teams in financial institutions are frustrated with the current processes, which are time-consuming, costly, and prone to errors. They deal with countless pages, data fields, and clauses that need to be processed daily. Banqora aims to solve this problem by automating and streamlining post-trade operations using machine learning and large language models. Their solution can process thousands of complex documents daily, significantly improving productivity and reducing costs for financial institutions.
Banqora: bringing AI-powered automation to post-trade processing
Banqora leverages machine learning and deep system integrations to solve the inefficiencies in post-trade processing. By automating the retrieval, extraction, and processing of legal documents for financial transactions, Banqora significantly reduces manual intervention, freeing up staffing resources while improving processing speeds. Their platform goes beyond basic document extraction by also integrating the Common Domain Model - an industry standard for trade data handling. This enables seamless transitions between stages of post-trade processing, improving the overall efficiency and accuracy of trade resolution.
Banqora’s initial focus is tailored to the specific needs of Tier 3 institutions, providing them with an affordable subscription model customised to their team size and needs. This allows smaller financial institutions to be more efficient, and compete more effectively, without the need for larger teams or new costly infrastructure.
Strong founder-market fit: Deep expertise in Finance, AI, and Intelligent Document Processing
Ernst Dolce, CEO: Ernst managed a €300B securities financing portfolio, which was supported by 8,500 legal documents. He was previously the Head of Liquidity Solutions & Head of Structuring at AXA IM - meaning he deeply understands the problem space of post-processing trades.
Ziad Al-Ziadi, CPO: Ziad is a VC-backed, ex-founder with 8 years in technical product management across machine learning and data. He previously built machine learning to extract data from 100,000 documents/month, making him the perfect person to lead Banqora’s product development.
Nicholas Holden, CTO: Nicholas is a financial engineering leader with 15+ years’ experience. Led the global engineering team at AXA IM, delivering advanced financial platforms. Nicholas has a PhD in Computer Science, with a focus on machine learning.
Antler’s investment thesis
Working with the Banqora team has been a truly rewarding experience. Ernst is relentless as a commercial founder and leader, demonstrating an unwavering commitment to driving the business forward. Ziad, a true product expert, complements this by bringing deep technical knowledge and innovation to the table. Both have shown incredible resilience and grit, navigating the challenges of a startup with determination. The recent addition of Nicholas has completed the team, bringing in the technical and machine learning expertise needed to propel Banqora forward. Their ability to bring on exceptional advisers and engage with typically hard-to-reach financial institutions at such an early stage is a testament to their leadership and vision.
Banqora has already gained significant traction with key industry players. They’ve formed a partnership with a leading banking integration platform, and are currently piloting their product with asset managers who have billions in AUM. Within just a couple of months since Antler invested, Banqora has been backed by leading early-stage funds, and leaders in the financial services industry.
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