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Cleareye.ai and Surecomp partner to enhance AI powered trade finance automation

Cleareye Blog Banner, Title is "Cleareye.ai and Surecomp partner to enhance AI powered trade finance automation." New Partnership

New York, USA, April 16, 2024: Cleareye.ai today announced a strategic partnership with Surecomp, a leading provider of digital trade finance solutions providing automation of trade compliance and operations.  Both firms are acutely aware of the challenges faced by their customers engaged in complex and time-consuming cross-border trade. This is confounded by increasingly complex regulatory requirements such as the monitoring of trade-based money laundering (TBML) putting a strain on operational capacity and rising costs.  Offering comprehensive solutions to financial institutions worldwide, the new collaboration between Surecomp and Cleareye will specifically address the need for more streamlined and readily accessible data from within trade documents.  By intelligently automating the classification of trade documents and accurately extracting and validating their data, banks can enhance trade productivity and speed. This process helps reduce risks and ensures compliance through automated checks for trade-based money laundering (TBML) and other relevant red flags. Ultimately, it increases the banks’ ability to scale and manage growing volumes of trade efficiently. “This partnership is a significant step forward in our mission to revolutionize trade finance,” said Sarath Sasikumar, President & Co-Founder, Cleareye.ai. “By combining our expertise with Surecomp, we can provide financial institutions with a powerful and comprehensive solution that empowers them to navigate the complexities of trade finance with greater efficiency and confidence.” “Banks continuously need to stay ahead of the ever-changing patterns in illegal trade behaviour. Embedding AI-based compliance checks into the trade finance transaction is a prime use case for bringing artificial intelligence to banks’ day-to-day operations,” says Enno-Burghard Weitzel, Surecomp’s Chief Solutions Officer. “As we continue building our extended value to customers through strategic fintech partnerships, we are delighted to welcome Cleareye to RIVO.”

13 Tech Leaders Share Their Favorite Software Development Life Cycle Methodologies

Cleareye Blog Banner, Title is "13 Tech Leaders Share Their Favorite Software Development Life Cycle Methodologies" Cleareye CTO Chandrasekhar Somasekhar is among them

FORBES Technology Council Featuring Chandrasekhar Somasekhar, Chief Architect cleareye.ai,Aug 10, 2021. There are mountains of articles on the various software development life cycle methodologies, but nothing beats personal experience. While there may be no single SDLC methodology that fits all companies’ processes, there are tried-and-tested methods trusted by tech leaders across the industry. While some choose to stick with a single method that works well for them and their team, others combine methodologies to create a hybrid approach. In almost every instance, though, a tech leader settles on their preferred system only after testing various methods “in the field.” The industry experts of Forbes Technology Council are seasoned leaders who have experience with the many SDLC options, and each is clear on why the method they have chosen for their team is the most efficient and effective path for them. Below, 13 of them share the software development life cycle methodology that gets their top vote, and why. Evolutionary Architecture + Agile Building solutions based on Evolutionary Architecture principles and Agile practices has proven efficient. Solutions need an architecture that enables incremental change over time. The late decisions and abstract design principles of Evolutionary Architecture don’t impact self-organizing Agile teams as they follow Continuous Delivery practices, automated infrastructure provisioning and data migration.– Chandrasekhar Somasekhar, cleareye.ai, Inc. Learn More

Building the Team for Your Next AI Initiative

Cleareye Blog Banner, Title is "By failing to prepare, you are preparing to fail. - Benjamin Framklin." Building the team for your next AI initiative

The time to invest on a AL/ML initiative is now. Recent surveys indicate the pandemic has caused companies to invest in new AI initiatives. On the other hand studies indicates many enterprises still struggle to operationalize the AI initiatives they have started. Other than the data quality challenges, the one major reason for this struggle is most companies don’t have the right team composition or the right objectives set for the teams to take the models to production. Planning upfront for the right team composition with complementing skills and setting the objectives are critical for a successful rollout of an AI initiative and better ROI As a new AI initiative is kicked off, the first resources to come on board will be your data scientists / ML engineers along with the business teams to explain the data and the context. Most initiatives start small. The initial objective of this team will be to prove a hypothesis with a limited set of data to explore and build the initial models. The first challenge the team will face after the hypothesis validation is on how to train on a massive production data set. Now the data is no longer a CSV export, to which feature engineering can be applied, and models can be fit in a single machine. Data pipeline architectures to be defined for data delivery, and greater scalability. The infrastructure required for optimal extraction, transformation, and loading of data to be defined. Data pipelines ensure the right data is made available to the models. Parallel data processing and distributed training need to be addressed. You need the Data Engineer in your team to do the heavy lifting. Bring your Data Engineers to the team right from the beginning. Data engineering skills can effectively utilize the network, storage, and compute resources. The objective of any AI initiative is to optimize the business process as the inference from the models is used to drive decisions. As the case with any software development, modularity is important when deploying machine learning systems. Each task in the business process to be broken down and decided where to introduce the predictions (models). This will enable the team members to work efficiently on individual parts and also encourages reusability for future projects. Make sure you have Solution Architect to take up this responsibility of breaking down the modules, visualizing the information flow end to end and holds the vision of the end state architecture. Loosely Coupled Architecture lets the team work independently and deliver value. Solution Architects should ensure modularity in the ML systems. Solution architects work at the intersection of multiple disciplines and they have to be great communicators and with people skills. As with application development, ML initiatives are too governed with standard principles of operations, like versioning, automation, monitoring reproducibility, etc. In ML initiatives these principles have a different set of objectives. In S/W development, versioning is limited your code and scripts. For ML engagements along with your Code, Data and Model need to be versioned, monitored, reproducibility to be ensured, and ML-workflow steps are automated. Onboard MLOps engineer from get go to set the principles of operations. Data versioning includes your Data preparation pipelines, Features store, Datasets, Metadata. Model Versioning includes ML model training pipeline, ML model (object), Hyperparameters, Experiment tracking. Reproducibility to be ensured by MLOps team that the same ‘hyper tuning’ parameters are applied to your production models. Plan for the new monitoring needs. There will be data distribution changes (training vs. serving data), measuring the model drifting, computational performance of the model. ML initiatives involve a lot of experimentation and development. Plan for tracking, automating, and monitoring your experiments well ahead. Invest time on Governance and Security early in cycle QA engineers are usually seeded to the engagements. But the objectives and guidelines set for a QA Engineer on an AI initiative has to different from the typical application testing. As stated from ML-Ops.org, make sure to set your guidelines around tests for features and data, tests for model development, and tests for ML infrastructure. QA Engineers doesn’t need to be experts in algorithms. But it is important to understand feature importance and the correlation of features in the business context. Create test scenarios to measure the inference score while adding new features or dropping new features. Model metrics like RMSE are important, but QA Engineers plan to correlate with business metrics on the impact of a prediction, like % reduction in false positives, etc. Automated testing for end to end ML Pipeline to be planned as part of quality assurance. Discussed only about a few roles and objectives, but remember AI is a team sport, as quoted by Cassie Kozyrkov. There are other roles crucial for the successful role out, like your Decision makers, Product managers, Data Labelling team, UX Designers, etc. Will discuss these roles soonLearn More About the Author Chandrasekhar Somasekhar is the Chief Architect at cleareye.ai. He is responsible for product development, strategic technology direction, and implementing and governing solution architecture methodologies at cleareye.ai. Chandra defines and governs the enterprise solution architecture strategy. Additionally, he provides architectural direction. He is passionate about developing, mentoring, and motivating a high-performance team.

MarTech Series Interview with Sarath Sasikumar, Chief Operating Officer at Cleareye.ai

Cleareye Blog Banner, Title is "MarTech Series Interview with Sarath Sasikumar, Chief Operating Officer at Cleareye.ai."

Tell us a little about yourself Sarath…we’d love to hear about your role, a typical day at work?  Well, let me start by saying that I’m an entrepreneur first then a COO. Being an entrepreneur can give you joy and stress and anxiety at the same time. The most important part is the realization that it is an adventure, and you better buckle up for the unexpected. In my current role, I’m responsible for the company’s ecosystem strategy, global operations, talent management & other business enablement functions. I’m also the Chief Value Mentor, evangelizing a “Values & Culture First” mindset across the Company. The one thing which has remained unchanged throughout my career is the key values and culture I live every day which directly reflects in relationships with my colleagues, customers, partners and investors.We are a deep tech company in hyper-growth mode, and as a COO of the Company, it’s my responsibility to ensure that the entire company functions as one unit and delivers exponential value to our customers and investors. Like any other B2B startup at our stage, we also go through a series of challenges and bottlenecks in any number of areas — operations, finance, legal, talent, technology etc. The good news is that this is not our first rodeo. We didn’t start this fresh out of school. In my previous position, I was fortunate to play multiple roles starting with deep tech computer programmer, pre-sales, field sales, sales ops and finally directly working for the CEO of a multi-billion dollar digital company running programs that are very strategic in nature. The experience I gained is tremendous, and I’m able to successfully apply this at cleareye.ai.If you were to ask me about my top three priorities as a COO on any given day, I would say talent is number one, where I spend a disproportionate amount of time whether top-notch talent internally or externally. Partners and planning are my second and third priorities.  Our Partner ecosystem is very dear to me. I am passionate about building partnerships with entities that can jointly create unmatched value for our customers which otherwise would have been impossible to realize. And, regarding planning as my third priority,  one big learning lesson I carry from my past position is that nothing can beat planning. I make it a point to find “thinking and planning time” every day and plan moves accordingly. About 50% of the time things may not go as planned, but the very fact that I had that opportunity to plan ahead will enable me to activate plan B or C as context demands. That way I always have a strategy and chances are very small that something can take me by surprise. Marketing Technology News: MarTech Interview with Hunter Montgomery, Chief Marketing Officer at ChurnZero Can you take us through some of your biggest business enablement and strategy learnings through the years in tech?  While at UST, I had the opportunity to spend quality time to learn from Ram Charan, a world-renowned business advisor, author and speaker who has spent the past 40 years working with many top companies, CEOs, and boards of our time. I consider the learnings and insights gained from those sessions with Ram very foundational to how I approach business today.I consider the ability to deal with uncertainty is perhaps the paramount skill leaders must have to be successful in this age. This is particularly important when you play the role that of a COO. I am referring to uncertainties beyond operational uncertainties. As I mentioned earlier a key part of the strategy is “planning”.  This gives us the ability to anticipate and deal with all sorts of threats that may potentially drive our business off the cliff.One such tool/ strategy that I practice on a daily basis and encourage everyone to try is the OODA loop approach (observe–orient–decide-act). Originally a concept first used by U.S. Air Force Col. John Boyd to improve fighter jet performance, it squarely fits into day-to-day decision-making for any businesses, not just deep tech startups like cleareye.ai. A few thoughts on what it takes today to ensure better seamless interactions and business success across departments as sales and marketing evolves (given today’s largely remote selling / remote marketing environment) and the need for COOs to keep things integrated and in place.  Another key learning from my past to ensure coordination and transparency among various functions is to meticulously conduct a JPS (Joint Planning Session). I have seen people confusing a JPS with regular staff meetings or review meetings. The idea behind conducting a JPS is significantly different. Again, this is one of those key insights which I picked up from Ram Charan and also Sajan Pillai, the former CEO of UST Global and Founder of Season Two Ventures, and I put this into practice here at cleareye.ai with zero dilution. The very premise of a JPS is that when information is transparent to all parties at the same time, they can form a common understanding and situational awareness. The functional leaders present in the JPS are better equipped to make trade-offs and adjustments quickly. Decisions are made, silos are broken, leaders are empowered, and the groups will come together, and the company gets results. A few best practices you feel COOs in the tech market need to insure to help enable their sales and engineering departments run more smoothly?  You can have the best engineering product or solution in the entire world but what is the point if you don’t have a team who can market or sell it? The world is constantly changing, and it is truer in the case of technology companies. Unlike the old days, sales and marketing teams in deep tech companies are very tech and customer-savvy. They have a larger role to play beyond selling boxes. Of all the people, they stay closest to the problem. They understand what the market needs and most importantly they singlehandedly can help us determine when to “PIVOT”.  If Tech

Cleareye.ai joins ITFA & the Fintech Committee

Cleareye Blog Banner, Title is "Cleareye.ai joins ITFA & the Fintech Committee"

LOS ANGELES, April 26, 2021 /PRNewswire/ — cleareye.ai, a leading fintech platform that transforms banks into hyper-agile organizations, announced that it is joining the ITFA Fintech Committee. cleareye.ai, aiming to make a significant impact in the trade finance ecosystem, finds ITFA the perfect forum to achieving its mission. Cleareye.ai’s “ClearTrade®” is designed to make the trade operations and sanctions screening simpler and less riskier. ClearTrade® is an easy-to-use digital workbench and proprietary rules engine to automate workflow of trade finance screening and compliance processing. The rules engine provides industry leading coverage of ICC rules governing international trade.Cleareye.ai in partnership with ITFA is committed to creating a safe and transparent trade finance industry.Mariya George, Co-Founder and President at cleareye.ai says: “cleareye.ai is excited to join ITFA to help its mission to bring together banks and financial institutions engaged in originating and distributing trade-related risks. Our focus is to help reduce the TBML risks induced by the document-centric process, human-centered review, and dependency on bank personnel. Our AI and NLP breakthroughs allow processing unstructured data accurately at scale and validating against ICC rules, to significantly reduce false positives and missed red flags.” Sarath Sasikumar, Co-Founder and COO at cleareye.ai states: “ITFA is the perfect forum for a Fintech like us who want to make a significant impact on the Trade Finance ecosystem. cleareye.ai is excited to be part of ITFA’s incredible journey and hopes to create exponential value not only to its members but also to the larger Trade Finance Industry. Trade finance undoubtfully is a powerful money-making tool for financial institutions, but it comes with its share of risk associated with a potential for fraud, money laundering, and breaches of regulations. cleareye.ai uses advanced Artificial Intelligence (AI) to improve the effectiveness of Trade Finance Operations and Sanctions Screening processes while increasing productivity by up to 70%. The platform processes trade finance transactions documents, performs automated reconciliation against UCP and ISBP rules, and seamlessly integrates with Sanctions Screenings and TBML systems.” ITFA is committed to the continuous development and growth of the trade finance and forfeiting industries. It acts as a valuable forum for its 300+ members to interact and transact business together profitably and safely. As a part of its mission, ITFA is at the forefront of understanding the emerging changes, challenges, and opportunities in trade finance.“Artificial intelligence is being embedded in a growing number of front- to back-office processes. This is a key technology trend that benefits banks and their corporate / SME clients. cleareye.ai demonstrates how modern technology enables banks to speed up various key processes whilst redefining client experience at a fraction of the cost,” said André Casterman, Chair Fintech Committee, ITFA. “We have only just begun what will be a very exciting and transformative journey to incorporate the use of artificial intelligence into trade and trade finance. I am very happy that cleareye.ai has seen the potential for their advanced technology in these markets and to welcome them to our community.” Said Sean Edwards, Chair, ITFA. About cleareye.ai cleareye.ai is a game-changing, advanced artificial intelligence (AI) machine learning platform that enables banks to launch tailored products at a rapid pace. Headquartered in Los Angeles with offices in New York, Bahrain, and India, the company works to significantly simplify banking by leveraging advanced AI techniques. Its platform combines the power of Consumer Experience Sensing, Insights generation from Data, and Autonomous Automation powered by Artificial Intelligence. This will transform banks into hyper-agile organizations, that customers want to bank with, and employees feel proud of, that deliver exceptional customer service where customers expect it, drive short-term gains and long-term growth, and generate insights to sustain momentum at a digital scale. It offers products in 3 categories – Regulatory Tech, Growth Tech and Cost Tech. cleareye.ai was founded by leaders in global technology, representing decades of entrepreneurial and digital systems experience across a range of industries. For more information, visit www.cleareye.aiContact: contact@cleareye.ai

Fintech Podcast: Around The Coin

Cleareye Blog Banner, Title is "Fintech Podcast: Around The Coin with Mariya George, CEO & Co-founder, Cleareye."

Around The Coin: Interview with Mariya George, Co-founder & President of cleareye.ai In this episode, Faisal Khan sits down with Mariya George, the Co-founder and President at cleareye.ai, a leading fintech platform transforming banks into hyper-agile organizations using advanced AI techniques. Previously, Mariya worked for nearly 15 years at UST Global, a US-based digital technology and IT services provider. Most recently, she served as the Head of Banking and Insurance at UST where she was responsible for the industry group’s overall vision, strategy, and investment priorities. Before that, she led multiple consulting engagements with a focus on bridging business demand and technology solutions.Your browser does not support the audio element.Learn More

14 Important Initiatives Every Tech Leader Should Pursue In 2021

Cleareye Blog Banner, Title is "14 Important Initiatives Every Tech Leader Should Pursue In 2021." by Chandrasekhar Somasekhar, CTO, ClearEye

FORBES Technology Council Featuring Chandrasekhar Somasekhar, Chief Architect cleareye.ai,Mar 18, 2021. — In the tech industry, it’s not only the hardware and software that evolves quickly. Trends in project planning and tracking, productivity, customer experience, and more can quickly come and go. Developers and executives must stay up to date on all the latest skills and practices. If they don’t, they risk falling behind competitors, losing top talent and more. As leaders in the tech industry, the members of Forbes Technology Council have valuable insights on the latest trends. Below are the 14 initiatives they believe every tech executive should pursue in 2021 and why. Curating A System Of Technology Partners Focused innovations from startups can be like tech’s “Big Five” coming up with breakthrough platforms—but an important part of the business process is being able to quickly offer the benefits of these innovations. Bringing together the capabilities of multiple startups, building integrations and extending collaboration can create value for both the businesses and their customers much faster than working in isolation. – Chandrasekhar Somasekhar, cleareye.ai, INC. Learn More

Automation: The Next Evolution in Banking

Cleareye Blog Banner, Title is "Automation: The Next Evolution in Banking"

When’s the last time you visited a bank? Financial transactions used to take place on premise. From deposits and withdrawals to thousands of other mundane and complex financial transactions the average person makes, it either happened in person or it didn’t happen at all. Then, in 1967, Barclays of London placed an ATM on a street corner and everything changed. From internet banking, to the proliferation of credit cards, to virtual wallets and the emergence of blockchain technology, the world of finance continues to evolve at an exponential rate. Today, the average individual, business owner, or high net-worth banking client can conduct nearly, if not all, of their financial business without stepping foot in a bank. The popularity of the phrase “cashless future” says it all; the very machine that started the modern banking renaissance, the ATM, may soon be obsolete. In everything from interest rate calculation and loan handling to basic accounting, the story of commercial banking is the story of automation and innovation. Never before has there been a time where successful banks were not pushing the envelope, developing new ways to improve their systems and customer experience. To Everyone’s Benefit Automation helps banks, customers, and regulators save valuable time and money, often while producing superior results. For global banking, the potential for value creation is one of the largest across industries, as McKinsey estimates that AI technologies could potentially deliver up to $1 trillion of additional value, annually. Many areas in banking and finance are ripe for innovation: Banking regulatory and compliance Anti-money laundering (AML) and sanction screening Letter of credit and guarantees Cash management operations Transaction screening automation Automated payment operations Payment investigations Progress and Room for Innovation Over the next few years, banks will be leveraging automation to streamline their processes and reduce overhead, all while improving customer experience at every touchpoint. On the retail side, automation is already incredibly prevalent in low-level loan approval, account setup, customer support, and more. Over the next several years, banks are expected to continue their trend of reducing physical bank branches in favor of increased digital and automated touch points for their retail clients. On the commercial side, automation is expected to revolutionize lending operations over the next few years. With processes to automate loan approval, credit analysis, and even payment and payment investigations, banks will become far more agile in their ability to adopt, offer, and support new loan products when they become available. To put this into perspective, according to McKinsey’s Research, cost per customer is expected to be reduced by 10%, time to onboard is expected to be cut by 20%, costs per loan are expected to be reduced by 10% and loan processing time is expected to be shortened by 15–40% simply through implementing automation and collaboration platforms. Huge strides are being made every day toward this future. Consider commercial lending operations: banks throughout the country experienced a massive shuffle last year and into this year as they struggled to build out systems and procedures to process and ensure compliance on PPP loans. Those institutions which leverage automation tools such as the ones offered by cleareye.ai to streamline their PPP process benefited greatly. Furthermore, artificial intelligence is changing the game when it comes to fraud detection. Automated tools, trained properly, can scan through hundreds of millions of rows of financial transactions and identify patterns and inconsistencies where a human simply could not, both due to scale and complexity. Over the next several years, the prevalence of artificial intelligence and automated review of financial statements is only going to increase. As fraud becomes ever more sophisticated, the tools needed to fight it must become equally sophisticated. Automated and intelligent tools are the approach of the future.

Technology Tackles Fraud and Money Laundering in International Trade

Cleareye Blog Banner, Title is "Technology Tackles Fraud and Money Laundering in International Trade."

Fraud and money laundering is an ongoing problem in the United States, and it is getting worse as the years go on. A report released by the US Government Accountability Office (GAO) suggests that tougher regulations in the financial sector have caused organized crime groups to increasingly turn to trade as a means to hide payments.  The umbrella term for money laundering via trade transaction is Trade Based Money Laundering (TBML) and it is one of the primary means of cleaning money used by criminal organizations. It is believed that over-invoicing for shipments of goods is one of the primary means of TBML.  Statistics show that there has been a consistent decrease in bulk cash seizures in the United States. While some may think this is good news, the lowered numbers actually suggests that this may be due to the increased use of international funds transfers and wires across borders as part of TBML schemes.  The volume of TBML is difficult to determine. However, Global Integrity, estimates it may be worth as much as $2.2 trillion annually. The large volume of trade and the complexities of the transactions work against the limited resources of customs agencies making this an attractive avenue for criminals.  The problem is a global one and includes cash smuggling and use of shell companies for wire and transfer of goods. China’s shell companies are also known to play a prominent role in the wiring and transferring of goods. Narcotics trafficking, financial fraud and money laundering are all prevalent TBML activities.  Identifying Vulnerabilities The GAO has identified open account trading as being a target of primary vulnerability. These are transactions that are handled but not financed by the bank.  Although banks take measures to report suspicious activity, they have limited visibility to the underlying reasons behind the activity. And with open account trading accounting for 80% of international trade processed through financial institutions, the measures they are currently taking are clearly not enough.  While banks apply due diligence checks and carry out standard money laundering procedures, they do not review documents such as invoices, bills of lading or customs declarations in open account transactions. This makes detecting TBML activity nearly impossible. In fact, of the reports of suspicious activity banks filed in 2019, only .2% were related to TBML.  These banks follow Anti-Money Laundering (AML) compliance guidelines that gather intelligence but they never pull the information together into a meaningful understanding of the risk that is posed. Therefore, many suspicious transactions go unchallenged.  Technology Can Help Although things look bleak, there is hope in the form of technological advances. Blockchain tools are being used to provide more visible, tamper proof information across the supply chain. Automated and digitized document review is being integrated as well.  Banks are slow to adapt to these systems, probably due to the cost of replacing legacy IT systems. However, a change is happening slowly but surely.  There is also some progress on the government level as a proof-of-concept study was conducted by the US Customs and Border Protection in 2018 trialing the use of blockchain technology in the process of document submission for cargo entry.  However, in order for technology to succeed, it must be accompanied by an information exchange between different authorities, particularly across national borders. So far, there has been little being done to facilitate these exchanges.  Experts suggest that a shared digital platform could help provide customs official with information on trade transactions in real time. It’s a doable solution but it requires getting the right institutions on board as far as implementation and funding.  The cleareye.ai Solution cleareye.ai has a technological solution that can work to minimize TBML and simplify banking.  They offer a Trade Finance Compliance & Screening tool that makes Trade Finance Operations and Sanctions Screening less risky.  cleareye.ai provides a digital platform that processes transaction documents, performs automated reconciliation against UCP and ISBP rules, and seamlessly integrates with Sanctions Screening and TBLM systems. Its AI capabilities increases productivity by 70%, its flexibility makes it easy for banks to customize systems in adhering to UCP and ISBP rules and it eliminates false positives and false negatives to minimize compliance check errors.  cleareye.ai also has a Fraud Monitoring & Detection Platform. The platform is powered by advanced analytics and machine learning and whittles down false positives in SAR created by AML products. Their learning algorithms use large pools of data and advanced computer patterns to detect patterns that might go unnoticed by data scientists. The technology eliminates problems that may come up when businesses work with a foreign supplier. It cuts down on fraudulent correspondence, data theft, executive and attorney representation, smurfing, mule, triage, micro-segmentation, nested accounts and more.  TBML has become a huge problem all over the world. Fortunately, there are technological solutions available. It is hopeful that these will be successful in reducing cases of fraud and money laundering in today’s trade space. 

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