October 3, 2018 | Written by: Kathryn Guarini
Categorized: IBM RegTech Innovations
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Advances in artificial intelligence, blockchain, and cybersecurity are poised to transform the financial services industries. These technologies bring capabilities that speed risk modeling, automate fraud detection, ensure regulatory compliance, enable distributed trust, and protect sensitive financial information. Mounting pressures on financial institutions to drive out costs, improve customer experience, complete with emerging players, and comply with new regulation mean that adopting advanced technologies will not be optional.
Key areas for digital disruption within financial services
We see three key areas for digital disruption within financial services:
- New hybrid cloud platforms will offer unprecedented scalability, agility, and security, optimized for enterprise AI and blockchain.
- AI-driven predictive analytics and process automation will transform client on-boarding, financial risk, regulatory compliance, financial crimes, and fraud preemption processes.
- Financial institutions will connect with one another – and third parties – through emerging distributed trust ecosystems and marketplaces, driving unique technology requirements and new business models.
Pay close attention to cybersecurity
On the platform side, it’s exciting to me to see how old becomes new and truly transformed. Today, the IBM Z mainframe is used by 92 of the top 100 banks in the world because it supports the unique transaction processing, scalability, and reliability needs of the industry. Innovations like pervasive encryption and secure service containers extend IBM Z leadership in cybersecurity, which is why it runs IBM’s Blockchain network. Perhaps no industry more than finance is more concerned about maintaining data privacy. Fusion AI can help protect bank data that resides in multiple locations while driving insights for value globally. We train an AI model based on data in one geography, build a separate model based on data in another geography, then bring together only the model parameters (not moving any data) and merge them to create one AI model that can be deployed in both locations.
Transforming how financial institutions operate
Regulatory compliance today is a costly and labor-intensive business. Today, the process of ensuring compliance is highly manual. In fact, some banks employ over 10,000 people in compliance alone. We are working to automate the process of regulation extraction, knowledge capture, identification of changes, and reconciling regulations with controls by applying obligation extraction and natural language understanding. We have developed a new approach to detecting financial crimes and fraud using graph analytics and machine learning. These techniques enable a significant reduction in the high rate of false positives common in traditional scoring. A new financial market forecasting tool based on deep learning predicts price trends and market volatility with compressed learning times and requiring lower computational resources. A Dynamic Boltzmann Machine (DvBM) artificial neural network is used for time-series prediction, which mimics the human brain’s learning scheme.
Ensuring trust in global financial ecosystems
We anticipate the rise of decentralized ecosystems characterized by distributed trust. Various services such as micropayments or escrow can be offered as part of blockchain-enabled, multi-party exchange platforms inside industry ecosystems. Know Your Customer (KYC) is a mandatory process that every financial institution must adhere to for customer on-boarding. A blockchain-based shared KYC solution supports sharing of common information that accelerates onboarding, reduces KYC costs, and eliminates repetition of tasks. For compliance, such as GDPR, some pioneering banks are turning to pseudonymization to reduce risk.
The Netherland’s Rabobank has replaced identifying client data fields artificial identifiers, or pseudonyms, that is replacing a real name with a fictitious one, more specifically, they replace the customer names with the Latin names of flowers. In emerging markets, many individuals and businesses lack access to credit and savings. In Kenya, we’ve been working on a new approach to assess credit risk. We analyzed purchase records from a mobile device and then applied machine learning algorithms to predict creditworthiness, in turn giving lenders the confidence they need to provide microloans to small businesses. Once the credit score is determined, we used a blockchain, based on the Hyperledger Fabric, to manage the entire lending process from application to receiving offers to accepting the terms to repayment. IBM is also developing an open automotive transaction platform for mobility services based on blockchain technology. The Car eWallet consortium offers asset-backed value tokens as payment rails for autonomous payment of mobility services such as fueling, charging, and car sharing.
Advancing financial services
Today, advanced technologies are being rapidly adopted within the financial services industries. From the platform to the processes to new ecosystems, we believe AI, blockchain, and cybersecurity will be incredibly transformative. Let’s find opportunities to collaborate to advance these exciting new technologies.