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Microsoft Cloud for Financial Services — Architect Perspective

Financial institutions have embraced digital innovation at a recorded pace to adopt new ways of working, serving the financial needs of customers, and keeping the markets performing. Moreover, they have done so while still operating within their control frameworks and regulatory requirements needed to serve in all parts of the world.

According to the 2020 Frost and Sullivan Global Cloud User Survey, “multi-cloud adoption has skyrocketed among financial firms in the past year, up nearly 70%. In addition, hybrid cloud adoption — already higher among financial firms than other industries — is up 8%. In response to the pandemic, financial firms are accelerating their cloud journeys, knitting together disparate IT environments (on‑premises, edge, and multiple clouds) as a foundation for digitalization.”

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The Importance of Defining Fairness for Decision-Making AI Models

Defining fairness is a problematic task. The definition depends heavily on context and culture and when it comes to algorithms, every problem is unique so will be solved through the use of unique datasets. Algorithmic fairness can stem from statistical and mathematical definitions and even legal definitions of the problem at hand. Furthermore, if we build models based on different definitions of fairness for the same purpose, they will produce entirely different outcomes. 

The measure of fairness also changes with each use case. AI for credit scoring is entirely different from customer segmentation for marketing efforts, for example. In short, it’s tough to land on a catch-all definition, but for the purpose of this article, I thought I’d make the following attempt: An algorithm has fairness if it does not produce unfair outcomes for, or representations of, individuals or groups.

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Why Fairer AI Is Essential For Long-Term Survival

Introduction

In my most recent post, I covered some areas that I hope to see evolve in the next year and beyond. How we can do more with data across industries is, of course, an important consideration for data scientists, businesses, and society as a whole, as better models lead to improved products and services. 

When machine learning models for cancer diagnoses show promise, we naturally rally around this positive step and rejoice in the vision of a brighter future because it’s a victory that touches us all in some way. But there are many other ways AI can and must be used for good in the world, and in my next few posts, I want to use a financial services example that affects all of us, to show how that can be achieved. 

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Knowledge Graph Insights Give Investors the Edge

Refinitiv Labs has developed a global infrastructure database that uses knowledge graph insights derived from large volumes of mostly unstructured data. How can the Global Infrastructure API project assist decision-makers in the infrastructure sector as they plan for a sustainable and fair recovery post-COVID-19?

  1. Global Infrastructure API, the latest proof of concept by Refinitiv Labs, is a global infrastructure database, which links fundamental data and provides an API entry point for queries.
  2. The prototype leverages knowledge graph insights to interlink different Refinitiv datasets, including bonds, syndicated loans, project finance, Middle East and North Africa (MENA) infrastructure projects, and Belt and Road Initiative (BRI) data.
  3. Visit the Refinitiv Labs project portfolio to find out how developers, data scientists, and subject-matter experts collaborated to build this customer-focused proof of concept - and many more.

As the ripple effects of the coronavirus are felt across the global economy, affecting manufacturing, supply chains, and the movement of people and goods, capital projects, infrastructure owners, and investors are faced with significant challenges.

These challenges are likely to increase in the months ahead, with infrastructure investment expected to become a key tool for macroeconomic stabilization.

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Payments Architecture – Fraud Detection Example

Cloud technology is changing the way payment services are architectured. In this series we will be presenting insight from our customers on adopting open source and cloud technology to modernize their payment service.So far we've presented research-based architectural blueprints of omnichannel customer experience, integrating with SaaS applications, and cloud-native development solutions.  

In the previous article in this series we walked through the anti-money laundering physical architecture. 

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DevOps in Financial Services: Be Like Messi

“DevOps” continues to be the buzzword across technology departments in financial institutions. It means different things to different people, regularly including automation, change management, deployment, continuous delivery, culture change. Essentially, an “easy” fix where Development and Operations collaborate to save cost, improve productivity, and lower risk.

In practice, success rates in the DevOps transformation journey vary dramatically across the industry. Delivery managers tasked with scaling an organization’s capabilities have faced challenges in cultural resistance, disparate tooling, complex procedural changes, deep-rooted silos, and conflicting advice/recommendations across the industry.

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Database Use Cases

To learn about the current and future state of databases, we spoke with and received insights from 19 IT professionals. We asked, "What are some real-world problems you, or your clients, are solving with databases?" Here’s what they shared with us:

Industry

  • Financial Services

    • We operate in the capital markets industry. Use cases capture high-frequency datasets for electronic trading, surveillance, and regulatory reporting where you have to analyze and run analytics against trillions of data points. IoT and IIot are big industries around capturing data from sensors in manufacturing plants and helping customers to optimize the manufacture of semiconductors and doing predictive analytics when a machine might suffer a failure so a company can take action before a failure occurs.
    • Quite a few organizations with large legacy on-prem datacenter rise in demand for cloud database support and all of the work required to move to the cloud. Large banks and retailers pursuing cloud initiatives. Database changes that need to float through a software pipeline. Better use of cloud platforms.
    • Santander has banking systems of hardware mainframe databases with Cobol. They’re and need to modernize applications Work with them on augmentation. New services for applications and share between the two systems to address customer requirements, more interactive capabilities without replacing existing. Another cloud agnostic, Kaminos financial banking software cloud-native, cloud-agnostic system. We work on any cloud.

  • Retail

    • Customer journey and hyper-personalization for marketing across all industries — retail, financial services, oil and gas, Telcos. Retain and acquire more customers Instead of transactional these use cases get into behavioral aspect based on transactional and social. What triggers you to refinance with us? These are Greenfield applications. Classical use cases like risk management, fraud detection, and inventory management are getting rebuild with analytics context built right in. Current engines are unable to scale beyond 90 days-worth of data. Customers want one and two-year views to make more informed decisions.

  • Other

    • One of our customers is collecting events and incidents in cloud databases, providing their customers with real-time actions /alerts/insights, and enabling companies to make better use of cloud technologies. We allowed them to save a lot of money doing so, and also opened up new use cases within their software. Another customer (in the packaging industry) is using our IoT Data Platform to increase the efficiency of their manufacturing production by providing real-time insights and alerts to the shop floor operators who can then fix issues faster and more acutely.
    • 1) Genomics England: Working with the NHS, Genomics England is sequencing 100K genomes from patients with rare diseases and their families, as well as patients with common cancer. MongoDB provides the flexibility needed to store and analyze complex data sets together as the team seeks to deliver better diagnostic approaches for patients and new discoveries for doctors and scientists.

      2) Coinbase: as crypto and the blockchain continue to carve out real-world use cases--international payments, debt settlement, verification for sensitive systems such as voting, etc. — Coinbase has scaled its operation to handle the massive amount of data that can transfer during peak windows, requiring transactional guarantees at huge scale.

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