9 Financial Technology Trends for 2020

9 Financial Technology Trends for 2020

As digital transformation gains momentum in the financial services industry, players are rethinking their automation and business intelligence (BI) playbooks to streamline operations. Technologies like Artificial Intelligence (AI), robotic process automation (RPA), and big data analytics are replacing traditional banking systems that either no longer work or don’t work fast enough. There are significant interruptions ahead that will significantly impact areas like compliance, speed to implementation, user experience (UX), and process costs.

DataArt has compiled a list of key financial technology trends you should keep tabs on in 2020:

1. AI-driven RPA

Gartner predicts that there will be 224 RPA use cases in the financial sector by 2020, with 72% of controllers leveraging the technology. Industry players like banks, broker-dealers, asset managers, and wealth advisors have been using RPA to transform mission-critical business processes for some time now.

For example, a growing number of large banks use RPA to streamline onboarding workflows such as client background verification and document gathering. A bit of the ongoing process automation encompasses verification of client information and real-time tracking of client transactions in compliance with Know Your Customer (KYC) rules.

But the trend is increasingly gaining traction among smaller players like mid-market organizations, hedge funds, and private equity. Just like their larger counterparts, these enterprises are implementing RPA to automate employee onboarding, compliance, middle and back-office operations, customer service, and other core processes.

Only a few firms have adopted RPA, however. They've prioritized the automation of discrete tasks and departmental workflows. Initially, these organizations relied on attended RPA to automate tasks. That's changing today, with emails and database changes (rather than humans) initiating robotic processes.

Today, AI is playing a more prominent role in process automation. While originally financial institutions used Natural Language Processing (NPL) tools to automate data extraction, they're accomplishing much more today by incorporating Machine Learning (ML) into RPA.

They're abandoning rule-based automation for cognitive computing models.

We're also witnessing an increase in automation through APIs to integrate otherwise disparate business workflows. It's a much more reliable approach compared to traditional processes.

2. AI will play a bigger role in big data management

How is the finance sector taking advantage of the big data revolution that's already underway? Companies are now re-designing their data architecture and operating principles to power up business intelligence/analytics and leverage data from both structured and unstructured sources.

Industry players recognize the role of agile data and analytics in accelerating transformation. Thus, they're developing modular data architectures that can change dynamically and evolve at the speed of business. The end-game is to enable users to interact with data in near real-time and improve the quality of products/services.

Also, finance companies are now turning to augmented analytics (ML/AI tools for raw data analytics) and augmented data management. These advanced techniques are helping to automate routine, time-consuming workflows in big data analytics, letting data scientists focus on high-value tasks.

Moreover, organizations in the sector have to revise their data governance and security practices to meet more stringent data management regulations going forward. The General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), which is set to go into effect in 2020, are some of the stricter rules compelling industry players to change how the collect, store, transmit, process, or use personal data.

3. AI and ML in process automation

As the role of AI in algorithmic trading gets bigger, new ML-powered alpha-generation techniques will emerge. There will be a rise in the use of AI and ML to automate operations and aid decision making in asset management.

Also, intelligent business process automation will receive a boost as NLP-driven digital workers help to automate front- and back-end office functions in the financial sector. NLP models will increasingly streamline business workflows and enhance user interactions with machine-generated data.

Likewise, improved, integrated computer vision, NLP, and ML technologies will help develop much-advanced RPA tools. These integrations mean that institutions like banks are getting closer to developing full capacity for unattended process automation.

In KYC compliance, for example, you can expect a complete shift from semi-automated processes to 100% automation in which humans play no role in information retrieval or verification for decision making.

4. Interoperability of systems

As the rest of the world moves to integrated software systems, the financial sector has to abandon monolithic application architectures to achieve interoperability. What's required here is a transition to interconnected system components, which are much easier to deploy, change, and share across multiple business units in an organization.

Some of the latest trends to hit the financial world are micro front-end applications, OpenFin, Electron, and micro-services. Organizations adopting these technologies will continue to face a high standard of interoperability and user experience as they shift from monolithic software architectures.

5. A highly scalable cloud

Cross-cloud capability is already gaining traction in financial services. An even newer trend in the sector is cloud-agnostic architecture. A growing number of enterprises will be adopting containerized solutions with Kubernetes clusters. The objective here is to deploy cloud computing resources at scale.

We expect to see an increase in the active deployment of cloud providers and the reallocation of market share. Winners will be players that exclusively offer key differentiators, namely AI, blockchain, and data management.

Other cloud services will become commoditized, and as a result, companies will rely on a single cloud, for example, to cut costs. However, the option to outsource from other cloud providers will remain open.

Still, banks, broker-dealers, asset managers, etc. will utilize private or on-premise cloud architectures that can scale based on computing needs.

6. More blockchain applications in financial services

Blockchain has already reached maturity in finance. In 2020, the same is expected of other industries as they discover viable ways to leverage the technology.

So far, several large-scale finance corporations have launched various blockchain projects as they experiment with digital distributed ledger technology. We expect this experimentation to produce more enterprise applications based on leading blockchain platforms like Hyperledger and Corda.

Since cryptocurrencies have been volatile for some time now, we expect fluctuations to cause a decline in blockchain funding. Similarly, there will be more government regulations on blockchain-derived alternative assets in 2020.

7. Asset management: transformation of data warehousing with AI

At DataArt, we expect to see AI-driven, serverless data warehouses powering advanced data management, optimization, and in-depth analytics in the finance industry. Data lake configurations will be automated too. This automation lets asset managers quickly get started on set up, configuration, and utilization of centralized data hubs containing traditional (structured) and alternative (unstructured) data.

So far, ML is helping with unstructured document processing and outlier detection in asset management. Its role will expand into research and automated decision making in 2020. Moreover, AI will give asset and investment managers intelligent process automation tools they can deploy more successfully than RPA.

8. Mobile everything in the insurance industry

We expect major shifts in the insurance market to include the adoption of advanced technologies, a growing need for product personalization, and lower barriers to entry. The declining cost of technology, the swelling population of millennial consumers, and mobile everything will drive the key transformations.

To meet personalization requirements, industry players will need better tools to transform vast amounts of personal data into smart, actionable formats. Also, these organizations won't be able to deliver "smartphone-based access to everything" without first upgrading their legacy software and outdated data architecture.

As the business environment becomes more unpredictable, how will insurers cope with the ensuing market dynamics? We anticipate a surge in the appetite for on-demand solutions like the cloud and Platform as a Service (PaaS) in the industry to provide for volatility.

What about external factors that will potentially impact the insurance industry? One of the responses insurers are working on is the delivery of granular, personalized products to meet customer expectations.

9. Fintech for regulatory compliance and conversational banking

Fintech is catching on in the financial services sector, and compliance is one of the critical areas it's impacting.

Adherence to changing or new financial regulations has always been a challenge for organizations in the industry. But large financial institutions are turning to Regtech to address the problem. The technology is helping them to optimize their regulatory compliance teams, which presently may constitute up to 15% of an organization's entire workforce.

Today, there are new and established Regtech firms solving a range of compliance and regulatory problems, from Anti-Money Laundering (AML) to KYC regulations. They provide technologies for compliance verification, transaction monitoring, and compliance report generation.

We see AI conversational banking and chatbots increasingly taking the place of live support agents in handling user requests. These smart virtual assistants can transform account management, helping customers with signup processes, loan processing, and making transactions.

Better yet, investors will soon be engaging chatbots for financial advice. At the same time, intelligent agents will be autonomously tracking data on budgeting, revenue streams, taxes, and more.

Also, we're moving to an open banking world. Integrated information systems will enable financial service providers like banks and personal finance apps to share customer data (with the customer's approval).

There you have it! At DataArt, we believe these key trends will define the technological transformations likely to take place in the financial industry in 2020.

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