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👋 Welcome back to our Director of ML Insights Series, Finance Edition! In the event you missed earlier Editions you’ll find them here:
Machine Learning Directors inside finance face the unique challenges of navigating legacy systems, deploying interpretable models, and maintaining customer trust, all while being highly regulated (with a number of government oversight). Each of those challenges requires deep industry knowledge and technical expertise to pilot effectively. The next experts from U.S. Bank, the Royal Bank of Canada, Moody’s Analytics and ex Research Scientist at Bloomberg AI all help uncover unique gems inside the Machine Learning x Finance sector.
You’ll hear from a juniors Greek National Tennis Champion, a broadcast creator with over 100+ patents, and a cycle polo player who usually played on the world’s oldest polo club (the Calcutta Polo Club). All turned financial ML experts.
🚀 Buckle up Goose, listed below are the highest insights from financial ML Mavericks:
Disclaimer: All views are from individuals and never from any past or current employers.
Ioannis Bakagiannis
Background: Passionate Machine Learning Expert with experience in delivering scalable, production-grade, and state-of-the-art Machine Learning solutions. Ioannis can also be the Host of Bak Up Podcast and seeks to make an impact on the world through AI.
Fun Fact: Ioannis was a juniors Greek national tennis champion.🏆
RBC: The world’s leading organizations look to RBC Capital Markets as an modern, trusted partner in capital markets, banking and finance.
1. How has ML made a positive impact on finance?
Everyone knows that ML is a disrupting force in all industries while repeatedly creating latest business opportunities. Many financial products have been created or altered resulting from ML corresponding to personalized insurance and targeted marketing.
Disruptions and profit are great but my favorite financial impact has been the ML-initiated conversation around trust in financial decision making.
Up to now, financial decisions like loan approval, rate determination, portfolio management, etc. have all been done by humans with relevant expertise. Essentially, people trusted “other people” or “experts” for financial decisions (and sometimes without query).
When ML attempted to automate that decision-making process, people asked, “Why should we trust a model?”. Models seemed to be black boxes of doom coming to switch honest working people. But that argument has initiated the conversation of trust in financial decision-making and ethics, no matter who or what’s involved.
As an industry, we’re still defining this conversation but with more transparency, because of ML in finance.
2. What are the most important ML challenges inside finance?
I can’t speak for firms but established financial institutions experience one continuous struggle, like all long-lived organizations: Legacy Systems.
Financial organizations have been around for some time they usually have evolved over time but today they’ve found themselves one way or the other as ‘tech firms’. Such organizations must be a part of cutting-edge technologies in order that they can compete with newcomer rivals but at the identical time maintain the robustness that makes our financial world work.
This internal battle is skewed by the danger appetite of the institutions. Financial risk increases linearly (normally) with the size of the answer you provide since we’re talking about money. But on top of that, there are other types of risk that a system failure will incur corresponding to Regulatory and Reputational risk. This compounded risk together with the complexity of migrating an enormous, mature system to a brand new tech stack is, a minimum of in my view, the most important challenge in adopting cutting-edge technologies corresponding to ML.
3. What’s a standard mistake you see people make attempting to integrate ML into financial applications?
ML, even with all its recent attention, remains to be a comparatively latest field in software engineering. The deployment of ML applications is commonly not a well-defined process. The artist/engineer can deliver an ML application however the world around it remains to be not accustomed to the technical process. At that intersection of technical and non-technical worlds, I even have seen probably the most “mistakes”.
It is tough to optimize for the best Business and ML KPIs and define the best objective function or the specified labels. I even have seen applications go to waste resulting from undesired prediction windows or because they predict the improper labels.
The worst consequence comes when the misalignment will not be uncovered in the event step and makes it into production.
Then applications can create unwanted user behavior or just measure/predict the improper thing. Unfortunately, we are inclined to equip the ML teams with tools and computing but not with solid processes and communication buffers. And mistakes originally of an ill-defined process grow with every step.
4. What excites you most in regards to the way forward for ML?
It’s difficult to not get excited with the whole lot latest that comes out of ML. The sphere changes so steadily that it’s refreshing.
Currently, we’re good at solving individual problems: computer vision, the following word prediction, data point generation, etc, but we haven’t been in a position to address multiple problems at the identical time. I’m excited to see how we are able to model such behaviors in mathematical expressions that currently appear to contradict one another. Hope we get there soon!
Debanjan Mahata
Background: Debanjan is Director of Machine Learning within the AI Team at Moody’s Analytics and in addition serves as an Adjunct Faculty at IIIT-Delhi, India. He’s an energetic researcher and is currently excited by various information extraction problems and domain adaptation techniques in NLP. He has a track record of formulating and applying machine learning to numerous use cases. He actively participates in this system committee of various top tier conference venues in machine learning.
Fun Fact: Debanjan played cycle polo on the world’s oldest polo club (the Calcutta Polo Club) when he was a child.
Moody’s Analytics: Provides financial intelligence and analytical tools supporting our clients’ growth, efficiency and risk management objectives.
1. How has ML made a positive impact on finance?
Machine learning (ML) has made a big positive impact within the finance industry in some ways. For instance, it has helped in combating financial crimes and identifying fraudulent transactions. Machine learning has been an important tool in applications corresponding to Know Your Customer (KYC) screening and Anti Money Laundering (AML). With a rise in AML fines by financial institutions worldwide, ever changing realm of sanctions, and greater complexity in money laundering, banks are increasing their investments in KYC and AML technologies, a lot of that are powered by ML. ML is revolutionizing multiple facets of this sector, especially bringing huge efficiency gains by automating various processes and assisting analysts to do their jobs more efficiently and accurately.
Considered one of the important thing useful traits of ML is that it could possibly learn from and find hidden patterns in large volumes of knowledge. With a concentrate on digitization, the financial sector is producing digital data greater than ever, which makes it difficult for humans to understand, process and make decisions. ML is enabling humans in making sense of the info, glean information from them, and make well-informed decisions. At Moody’s Analytics, we’re using ML and helping our clients to raised manage risk and meet business and industry demands.
2. What are the most important ML challenges inside finance?
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Reducing the False Positives without impacting the True Positives – Various applications using ML within the regtech space depend on alerts. With strict regulatory measures and massive financial implications of a improper decision, human investigations might be time consuming and demanding. ML actually helps in these scenarios in assisting human analysts to reach at the best decisions. But when a ML system ends in plenty of False Positives, it makes an analysts’ job harder. Coming up with the best balance is a very important challenge for ML in finance.
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Gap between ML in basic research and education and ML in finance – Attributable to the regulated nature of the finance industry, we see limited exchange of ideas, data, and resources between the fundamental research and the finance sector, in the world of ML. There are few exceptions after all. This has led to scarcity of developing ML research that cater to the needs of the finance industry. I feel more efforts have to be made to diminish this gap. Otherwise, it’s going to be increasingly difficult for the finance industry to leverage the newest ML advances.
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Legacy infrastructure and databases – Many financial institutions still carry legacy infrastructure with them which makes it difficult for applying modern ML technologies and particularly to integrate them. The finance industry would profit from borrowing key ideas, culture and best practices from the tech industry with regards to developing latest infrastructure and enabling the ML professionals to innovate and make more impact. There are actually challenges related to operationalizing ML across the industry.
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Data and model governance – More data and model governance efforts must be made on this sector. As we collect increasingly more data there must be more increase within the efforts to gather top quality data and the best data. Extra precautions must be taken when ML models are involved in decisioning. Proper model governance measures and frameworks must be developed for various financial applications. An enormous challenge on this space is the dearth of tools and technologies to operationalize data and model governance which might be often needed for ML systems operating on this sector. More efforts must also be made in understanding bias in the info that train the models and how you can make it a standard practice to mitigate them in the general process. Ensuring auditability, model and data lineage has been difficult for ML teams.
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Explainability and Interpretability – Developing models that are highly accurate in addition to interpretable and explainable is an enormous challenge. Modern deep learning models often outperform more traditional models; nonetheless, they lack explainability and interpretability. Many of the applications in finance demands explainability. Adopting the newest developments on this area and ensuring the event of interpretable models with explainable predictions have been a challenge.
3. What’s a standard mistake you see people make attempting to integrate ML into financial applications?
- Not understanding the info well and the raw predictions made by the ML models trained on them.
- Not analyzing failed efforts and learning from them.
- Not understanding the top application and the way it’s going to be used.
- Trying complex techniques when simpler solutions might suffice.
4. What excites you most in regards to the way forward for ML?
I’m really blown away by how modern ML models have been learning wealthy representations of text, audio, images, videos, code and so forth using self-supervised learning on large amounts of knowledge. The long run is actually multi-modal and there was consistent progress in understanding multi-modal content through the lens of ML. I feel that is going to play an important role within the near future and I’m excited by it and looking out forward to being an element of those advances.
Soumitri Kolavennu
Background: Soumitri Kolavennu is a SVP and head of AI research in U.S. Bank’s enterprise analytics and AI organization. He’s currently focused on deep learning based NLP, vision & audio analytics, graph neural networks, sensor/knowledge fusion, time-series data with application to automation, information extraction, fraud detection and anti-money laundering in financial systems.
Previously, he held the position of Fellows Leader & Senior Fellow, while working at Honeywell International Inc. where he had worked on IoT and control systems applied to smart home, smart cities, industrial and automotive systems.
Fun Fact: Soumitri is a prolific inventor with 100+ issued U.S. patents in varied fields including control systems, Web of Things, wireless networking, optimization, turbocharging, speech recognition, machine learning and AI. He also has around 30 publications, authored a book, book chapters and was elected member of NIST’s smart grid committee.
U.S. Bank: The most important regional bank in america, U.S. Bank blends its relationship teams, branches and ATM networks with digital tools that allow customers to bank when, where and the way they like.
1. How has ML made a positive impact on finance?
Machine learning and artificial intelligence have made a profound and positive impact on finance typically and banking specifically. There are a lot of applications in banking where many aspects (features) are to be considered when making a choice and ML has traditionally helped on this respect. For instance, the credit rating all of us universally depend on is derived from a machine learning algorithm.
Over time ML has interestingly also helped remove human bias from decisions and provided a consistent algorithmic approach to decisions. For instance, in bank card/loan underwriting and mortgages, modern AI techniques can take more aspects (free form text, behavioral trends, social and financial interactions) into consideration for decisions while also detecting fraud.
2. What are the most important ML challenges inside finance?
The finance and banking industry brings plenty of challenges resulting from the character of the industry. To start with, it’s a highly regulated industry with government oversight in lots of features. The information that is commonly used could be very personal and identifiable data (social security numbers, bank statements, tax records, etc). Hence there’s plenty of care taken to create machine learning and AI models which might be private and unbiased. Many government regulations require any models to be explainable. For instance, if a loan is denied, there’s a fundamental need to clarify why it’s denied.
The information then again, which could also be scarce in other industries is abundant within the financial industry. (Mortgage records must be kept for 30 years for instance). The present trend for digitization of knowledge and the explosion of more sophisticated AI/ML techniques has created a novel opportunity for the appliance of those advances.
3. What’s a standard mistake you see people make attempting to integrate ML into financial applications?
Some of the common mistakes people make is to make use of a model or a way without understanding the underlying working principles, benefits, and shortcomings of the model. People tend to consider AI/ML models as a ‘black box’. In finance, it is particularly essential to grasp the model and to give you the chance to clarify its’ output. One other mistake will not be comprehensively testing the model on a representative input space. Model performance, validation, inference capacities, and model monitoring (retraining intervals) are all essential to contemplate when selecting a model.
4. What excites you most in regards to the way forward for ML?
Now’s a fantastic time to be in applied ML and AI. The techniques in AI/ML are actually refining if not redefining many scientific disciplines. I’m very enthusiastic about how all of the developments which might be currently underway will reshape the long run.
After I first began working in NLP, I used to be in awe of the power of neural networks/language models to generate a number or vector (which we now call embeddings) that represents a word, a sentence with the associated grammar, or perhaps a paragraph. We’re continuously in the hunt for increasingly more appropriate and contextual embeddings.
We now have advanced far beyond a “easy” embedding for a text to “multimodal” embeddings which might be much more awe-inspiring to me. I’m most excited and stay up for generating and twiddling with these latest embeddings enabling more exciting applications in the long run.
🤗 Thanks for joining us on this third installment of ML Director Insights. Stay tuned for more insights from ML Directors.
Big because of Soumitri Kolavennu, Debanjan Mahata, and Ioannis Bakagiannis for his or her good insights and participation on this piece. We stay up for watching your continued success and can be cheering you on each step of the way in which. 🎉
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