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Given DataSkill’s success within the market, explain how DataSkill’s relationship with IBM has helped shape the client’s business structure?
As a native AI company, DataSkill has witnessed the growth and transformation of AI and automation from its preliminary designs to its current phase. With that evolution in mind, DataSkill is focusing on leveraging AI in the financial sector harnessing its Acumi technology with IBM’s products to adjudicate multi-party contract-based business processes, especially with high variety and low-quality documents. This relationship with IBM is central to DataSkill’s strategy due to three fundamental points. First, IBM’s products are well proven. Second, IBM’s global presence has helped introduce DataSkill to high growth areas like Asia-Pacific. Third, IBM’s client relationships with the largest banking and financial clients have helped open doors so DataSkill can understand market needs and build the platform accordingly. But, even with IBM’s prowess, it is hard to provide a niche solution to every type of problem in the finance industry. Such harmony opens up innovations from IBM’s partner networks—and DataSkill to specialize in state-of-the-art fintech applications that fit specific client requirements.
IBM is a global player with a broad solution portfolio and cutting-edge technology on which partners build their technology to cater to specific problems. In the global trade finance, DataSkill is transforming the manual process of maker-checkers into an automated process.
DataSkill became IBM’s partner when Deep Blue had a near-winning chess encounter with Gary Kasparov in 1996 and shifted its entire focus to AI in 2011 after IBM’s Watson successfully won the Jeopardy show.
Please elaborate on how DataSkill leverages IBM’s technology.
The trillion-dollar global trade finance system still runs on a paper-based document-centric system. In fact, the financial institutions spend over $50B dollars per year on manual document checking, which provides a big automation opportunity in the market. Since the majority of the clients are already utilizing IBM’s technology, it is quicker for DataSkill to deploy an intelligent document solution that leverages IBM’s portfolio.
Underpinning Acumi solution with IBM products helps clients to scan, classify, and interpret important documents to adjudicate multi-party contracts
As the entire financial industry depends on unstructured data, clients leverage IBM Watson’s suite of products such as Watson Explorer that uses natural language processing to search documents and provide insightful data. These technologies have become important pieces in the journey to digitize documents. On top of this, the DataSkill’s Acumi software uses a supervised learning algorithm that learns unobtrusively from bank knowledge workers in real-time. Acumi, being AI-powered, has the capability to understand unstructured data even when the data is not completely clean. The software scans the documents constantly and points out inaccuracies such as missing decimals in invoices, incomplete forms, the difference in formats, and credentials marked in the wrong box.
The Acumi software suite is comprised of Acumi DataFind, Acumi DataLearn, and Acumi DataConnect. Acumi DataFind concentrates specifically on the trade market and spatial ontology to automate the categorizing, and classifying of documents. Acumi DataLearn uses knowledge analysis to extract entities and constantly learn from mistakes, format changes, and any other types of discrepancies that exist in the data. The DataLearn algorithm understands, not what, but how the end-user is doing certain things to eliminate human intervention in the future. Acumi DataConnect uses IoT technology and for trade finance tracks data from sensors attached to shipping containers or carriages from the warehouse to the delivery location. The accuracy of the Acumi algorithms is a key factor in enabling smarts contracts within distributed ledger and blockchain networks.
What are the few ongoing challenges within the financial market and how DataSkill is the right solution to solve said points?
There are numerous challenges in the digital banking revolution. In point witness the struggle to scale the performance of knowledge workers to improve customer relationships and meet regulatory compliance. Banks and corporates employ DataSkill to supercharge the performance of each knowledge worker so they can recruit and retain customers and gain more accuracy in the areas of compliance and counter-crimes. This latter area reduces exposure in fines, prosecution and most importantly their reputation which is priceless.
Please give us a customer success story that defines the proficiency of DataSkill.
DataSkill was approached by a global banking client with several thousands of marker-checkers who were constantly reading, interpreting, and integrating trade details into their transaction system. This highly manual process is often prone to error and inefficiency. The bank was unable to hire more knowledge workers that understood letters of credits issued by banks, a core instrument for executing global trade finance. The growth rate of the client was hampered and the inaccuracies in document filing due to manual error created major penalties for the bank. With DataSkill, the client was able to prevent such inaccuracies with their AI-powered Acum software as well as solve sanction and compliance issues in regards to regulatory, counterfeit, and anti-fraud.
Since 2016, focus on the financial industry has helped DataSkill realize the shortcomings of the market, and aim to improve the efficiency of knowledge workers as well as increase an organization’s ROI.
As an IBM partner for over 21 years, DataSkill continues to build on this long-standing relationship and collaborate with them to expand the deployment of solutions. DataSkill’s aim is to simplify the implementation of the automation process on a global scale.