#contractanalysis

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Gartner defines dark data as the information assets organizations collect, process and store during regular business activities, but generally fail to use for other purposes (for example, analytics, business relationships and direct monetizing). Similar to dark matter in physics, dark data often comprises most organizations’ universe of information assets. These assets include emails, contractual documents, multimedia, and other intellectual property. Parsing, tagging, linking and searching all this dark data is the greatest immediate opportunity for most businesses to unlock the value. With the advances in NLP and Machine learning, making sense out of this data and generating actionable insights to surface the risk, liabilities, and opportunities to renegotiate contractual terms is a low hanging fruit to prove the ROI of the AI initiatives in a company. It’s a good idea to enrich an organization’s data with external data sources that will help in creating new services. The data from Experian, Nielsen and other data aggregators are being used by companies that provide identity theft management, social lending platforms, and others to make instant decisions by applying various machine learning models.

Every organization enters into agreements with the vendors from whom they take services. For various reasons most of these vendor agreements have renewal terms in such a way that unless there is a prior written notice within X days of the agreement termination, the agreement that the organization has signed gets auto renewed for another term. This can become a potential risk especially when the organization is not very happy with the services and wanted to terminate but has lost track of the agreement dates resulting in auto renewal of the signed agreement.

This is a great area where deep learning is being applied to solve several business problems and a great helper in the digital journey of an Enterprise.

Enterprise Cognitive Computing is the use of AI in enhancing and scaling business operations for any company and any industry. This involves incorporating AI into business process flows that accomplish certain tasks – typically these are the tasks that are well defined, repetitive and mundane in nature. For example, a cognitive automation process can help a mortgage loan officer to determine the loan eligibility of a borrower, which is a critical step in the home buying process and shortens the turnaround times. Typically, the borrower needs to submit pay stubs, employment letters, tax and bank statements and several other supporting documents by uploading into a loan portal or by submitting these via email. An ML / NLP based tool that can extract information from these unstructured documents would greatly reduce human hours and populate the key data such as gross salary, employer and employee details, length of employment, taxes paid and other relevant information combined with credit scores that can be further fed into an algorithmic decision-making engine.

The world is going through a lot of advancements in technology and making a huge impact on how business is being conducted and how organizations are adapting to these new technologies. A lot of human-intensive tasks are being fully automated ever since Artificial Intelligence and Machine Learning have come into usage. we are seeing routine and mundane tasks being automated with the help of RPA (Robotic Process Automation), a lot of customer support functions getting automated with the help of Voice BOTS. It is not very far that we will witness wide usage of a Robot taking instructions from a recognized human voice and perform a business transaction on the fly. Still, Departments like Sales, Procurement, Legal, Human Resource across organizations who are constantly handling Customers, Vendors, Employees are not making full use of the latest technologies like AI and ML in solving their day to day business challenges. Most of these have adapted to automation in the work they perform. For example, if you consider a procurement department entering into agreements with different vendors they have probably used technology in automating their contract management process like drafting a contract, sharing the draft with stakeholders for reviewing and redlining, negotiating contract terms and finally authoring the contract with a digital signature.

Gartner defines dark data as the information assets organizations collect, process and store during regular business activities, but generally fail to use for other purposes (for example, analytics, business relationships and direct monetizing). Similar to dark matter in physics, dark data often comprises most organizations’ universe of information assets. These assets include emails, contractual documents, multimedia, and other intellectual property. Parsing, tagging, linking and searching all this dark data is the greatest immediate opportunity for most businesses to unlock the value. With the advances in NLP and Machine learning, making sense out of this data and generating actionable insights to surface the risk, liabilities, and opportunities to renegotiate contractual terms is a low hanging fruit to prove the ROI of the AI initiatives in a company. It’s a good idea to enrich an organization’s data with external data sources that will help in creating new services. The data from Experian, Nielsen and other data aggregators are being used by companies that provide identity theft management, social lending platforms, and others to make instant decisions by applying various machine learning models.