Virtual Internship Opportunity: Computer Science

Positions available: 1 team of 2 person Our organization is able to offer a virtual internship opportunity for computer science students or teams. The primary focus for the student will be: (please include a few examples of technology projects you would like the student(s) to execute during the placement, as exemplified below. Where applicable, please indicate tools/platforms the student(s will be using to complete their work for you.) Operartis is deploying its machine learning java and Kotlin based solution to the cloud-based financial services platform https://www.fusionfabric.cloud . We are seeking students to assist with the end to end API build, Finastra validation process and launch on the Finastra app store. Our goal at the end of this experience is: For the student(s) to create a fully functioning API for Matchimus into the Finastra cloud environment and to see this application through the official Finastra validation process, document the API and testing and assist in the launch into the application store by a deadline of May 31st 2021.

Execute first benchmarks on transaction matching for financial institutions

ABOUT COMPANY Founded in New York by veterans of the banking industry our mission is to provide machine learning-based technology solutions that push automation and straight-through processing to the next level, driving down the need for tedious manual work and freeing up the most precious resource an organization has: its people. Our flagship product is our groundbreaking machine learning based match rate booster for reducing the manual effort needed for transaction reconciliations. PROJECT SCOPE Problem Background: AI and machine learning are making more inroads into the financial services industry. Beyond the established use of AI for fraud detection and anti-money laundering, the use of AI and ML continues to push into other business cases, including the use for bank back office processes such as transaction reconciliations used for financial control and reporting. For example, those performed for matching bank transactions to business activities such as expenses, invoices or trading. One of the difficulties that customers have in evaluating such ML based products is that the business value of these products is defined by the efficacy and accuracy of the ML model rather than being based on the particular product features (as it is for standard procedural software systems). Traditional research / product advisory firms such as Gartner, Celent, EY, Accenture etc create feature based vendor product evaluation reports, but none as yet provide any quantitative measurement of the ML functionality in vendor products. This project aims to setup the first industry benchmark for this ubiquitous business problem. Project Goal: Take existing Operartis data sets and clean and anonymize these for external use. Formally document these datasets such that external parties can utilize the datasets and set them up in their reconciliation engines. Create tools to show the match rate, accuracy and reliability curves and design other repeatable metrics in line with benchmark best practices. Optional additional goals: Collaborate with advisory firms to help execute the benchmarks for different customers. Assist with outreach to leading vendors for reconciliation engines (Operartis will provide contact details for vendors) (Optional) Create a consortium of the leading vendors to help design the different data sets and benchmarks. Where possible evaluate the market leading reconciliation engines on these data sets. Generate a report on the findings. Data set sources include: Operartis example data sets. Simulated data generation (Operartis has an existing data simulator which can be used to generate a variety of test data sets.) Other vendor data sets. https://fintechsandbox.org  Real world financial data sets Internal organization data sets (e.g. from your university) You will work with the core Operartis team and meet weekly with the CEO as the project progresses and will be able to adapt, augment this general plan as the project progresses. The Operartis team is a compact, friendly team where you will have a welcoming place to express your creativity and problem solving skills.

INSIGHT-SELLING AND PARTNERSHIP STRATEGY

ABOUT COMPANY Founded in New York by veterans of the banking industry our mission is to provide machine learning-based technology solutions that push automation and straight-through processing to the next level, driving down the need for tedious manual work and freeing up the most precious resource an organization has: its people. PROJECT SCOPE Our organization has developed a novel machine learning-based solution to a pain point for financial institutions during transaction reconciliation processes. (The most prevalent and most widely known example of a transaction reconciliation is a cash reconciliation which is performed between business activity transactions such as expenses, invoices, trades etc and the associated cash movements.) We would like to improve/develop the following in order to grow our sales: Prospecting Strategy - Evaluate our current leads base and make recommendations for further market penetration; assess our current outreach process and develop and implement targeted prospecting; review our prospect messaging, make improvement recommendations, and create new outreach content. Review and improve our existing sales process and sales playbook. We recently developed the industry's first survey/assessment which focuses on and quantifies this industry pain-point and would like to use this survey and it’s finding to create an insights-driven sales process . We also just launched datasets for use for benchmarking of machine learning matching solutions which we would like to use incorporate into our sales process a scientific proof-driven style of selling . Develop channel partner, affiliate marketing and platform as a service strategy. We already established a partnership with one of the Big 5 consulting firms and would like to continue building out relationships with other partners. We would like to develop a well-constructed, attractive and standardized value proposition, referral program, revenue share model etc, co-branding. You will work with the core Operartis team and meet as frequently you need to with the CEO as the project progresses. The Operartis team is a compact, friendly team where you will have a welcoming place to express your creativity and problem solving skills.

SEO Strategy, Content calendar

We would like to drive more traffic to our website. We would like to be on the first page of google for manual transaction questions. We know that we need to implement SEO, and we have improved some of our keyword searches but are still not ranking highly on some key terms. We would like to collaborate with students to understand what we need to do to generate more traffic and revenue through search engines. Complete an SEO analysis on our website Conduct competitive keyword research Update our website with the best structure and elements for SEO. Develop an on-page and off-page SEO plan. Identify key metrics to track for success. Analyse visitor characteristics, make suggestions to improve visitor engagement and click through Planning a content calendar to drive traffic to our site. Placement of surveys.