Hitachi Vantara’s VP and general manager of ASEAN, answers some questions Enterprise IT News has about the banking industry and the technologies it can potentially leverage.
EITN: How has apps and online banking revolutionised the finance industry?
Joe: Accelerated digitalisation has changed many aspects of our economy, society, and environment for the good – this includes the finance industry. Online banking and apps, for instance, have changed the ways in which financial transactions are conducted on a daily basis. Online banking capabilities typically include the ability to:
Conduct online banking transactions, such as viewing account balances and history, making transfers, cancelling checks, viewing check images, and accessing electronic statements.
View aggregated account information
Receive transaction and spending alerts
Engage in online chat for service
Manage personal and business finances online and download information to accounting or tax presentation software
Manage user access and functions like security
However, apart from operational efficiency, another way in which online banking and apps have revolutionised the finance industry is through its ability to redefine customer experience.
Online banking offers convenience – consumers have the ability to deposit checks, check their balance, and transfer funds, wherever and however they want.
At the same time, features in online banking such as access to savings tools, personalised financial advice, or virtual assistants add value to basic daily banking transactions and account management.
EITN: How are some of the Big Data technologies, like artificial intelligence (AI), machine learning and natural language processing proliferating in finance functions?
Joe: In today’s world, financial institutions have access to a plethora of data – this data needs to be extracted, structured, processed, analysed, and protected. This is one of the many places where we are seeing a proliferation of big data technologies such as artificial intelligence (AI), machine learning, and natural language processing. The need for big data technology in the finance industry is immense: apart from enhancing operational efficiency through automation, big data technologies reduce human biases, automates highly manual tasks, and improves the speed and accuracy of strategic decision-making processes.
Apart from data management and analysis, financial institutions are leveraging technologies for tasks such as fraud detection, predictive analytics, risk assessment, algorithmic stock trading, and customer relationship management, amongst others.
Ultimately, a resilient infrastructure that ensures 100% data security and reliability is crucial for all the financial institutions to secure highly sensitive and confidential data. Without a robust foundation, the advanced technologies cannot deliver the desired business outcomes.
EITN: How well positioned is Hitachi Vantara in offering solutions and services to Malaysia’s digital bank awardees?
Joe: The arrival of digital banks will spur the digital transformation in Malaysia. With BNM’s virtual banking licensing framework taking flight from June 2020, the banking industry has been paying more attention to the capabilities needed for digitalisation. Banks began observing more paperless, branchless, and signatureless transactions over the web. Digital banks make banking easier, faster, more convenient and affordable.
Ultimately, a resilient infrastructure that ensures 100% data security and reliability is crucial for all the financial institutions to secure highly sensitive and confidential data. Without a robust foundation, the advanced technologies cannot deliver the desired business outcomes.
Hitachi Vantara has worked with large and medium-size banks to rearchitect their digital core to foster rapid innovation. The top five technology innovations that underpin a banking innovation platform includes:
Having open APIs for Access – APIs are the fundamental building block of flexibility. They provide the means to access data and critical application functionality, which are the foundation for providing customers with smooth, efficient, modern performative experiences.
Creating a Robotic Process Automation system – Process automation codifies that operation so that the correct calls are made without requiring the involvement of any staff so that the customer’s intention is well-understood and their experience flows unimpeded.
Artificial intelligence for mining data – AI makes it possible to mine data from processes in action. It creates opportunities to monetise that data by presenting the customer with additional service offers, retaining customers, or recognising macro-level patterns across groups of customers that suggest regional options.
Data and Application Integration – Information must be moved into a system composed of data warehousing and digital asset management capabilities to record, store and recall information on demand.
Modern and Automated Infrastructure, Edge to Cloud – The infrastructure needs to be highly available, responsive, secure and compliant while being distributed across multiple clouds, data centres and even branch office systems.
EITN: How does Hitachi Vantara foresee advancements in the personalisation of finance services and products?
Joe: With transactional capabilities in place, digital banking leaders are now shifting their focus from developing features to creating personalised user experiences that increase customer retention and drive revenue growth.
By using analytics, banks can identify customer preferences and accelerate digital access to information, which helps them to pinpoint crucial features for a better consumer experience.
To meet today’s challenges, financial services organisations are looking to bring data resources together in a way that “seems seamless” to the customer.
Financial services organisations try to create highly personalised customer experiences, by understanding their customer through user segmentation and targeting, optimising their existing processes through automation and increasing cybersecurity and achieving better risk management.
Financial services executives need to rethink their approach to data and create new ways to identify, secure and flow high-value information through their organisations. Data operations – or DataOps – an emerging practice, is the answer to how to approach data in a new way.
DataOps changes the game for risk management, as well as customer understanding, acquisition and retention by ensuring greater accuracy for next-best action decisions. DataOps also paves the way to better meet regulatory requirements and ultimately helps financial services organisations compete in today’s highly competitive, complex environment.
Financial services organisations try to create highly personalised customer experiences, by understanding their customer through user segmentation and targeting, optimising their existing processes through automation and increasing cybersecurity and achieving better risk management.
EITN: What is the future looking like for the finance industry with a leap in growth in the use of AI and data analytics?
Joe: There is a rise in the emergence of data and artificial intelligence (AI) centric organisations. They are adopting advanced analytic models and machine learning (ML) methods to create next-generation, agile, data-powered products and services.
There are three strategic technologies that provide a foundation to support the transition from existing infrastructure to an agile, data-driven environment:
Agile data flows enable the operationalization of analytical and ML models, without significant development. At the same time, they maintain the agility of data pipelines, allowing data to iteratively flow freely through the organisation and move away from static and batched data pipelines.
Converged analytics and ML provide a powerful, unified, easy-to-use data modeling and analytical environment. This environment can be deployed in a wide range of scenarios, including quantitative research, business analyst functions and operations activities, enabling data to drive agility at all levels within the organisation.
High-speed data movement transitions data from data warehouses and data lakes to hybrid data architectures, such as “data lakehouses.” Leveraging high-speed data substrates allows raw data to be ingested and passed directly to analytic data engines and ML functions in near real time.
The use of these technologies underpin the future of the finance industry, as it continues to harness the advancements of AI and data analytics in their solutions and services.
The key challenge facing organisations that are developing these advanced products and services is to evolve their data infrastructure. They need to enable larger computational and data resources, which are required to run algorithms that underpin analytical methods.
EITN: How can organisations keep up with the implementation of Big Data in their financial offerings? What are the key strategies involved?
Joe: As financial services organisations look to embrace much broader data-led strategies, they are combining both transactional and non-transactional data with advanced machine learning (ML) algorithms, to drive intelligent capabilities across all products and services.
The key challenge facing organisations that are developing these advanced products and services is to evolve their data infrastructure. They need to enable larger computational and data resources, which are required to run algorithms that underpin analytical methods.
These methods are foundational to the future of digital financial services.
This new course in data architecture will provide a number of key foundational capabilities that enable a converged approach, which is based on:
High-performance data architecture. This reduces the need for complex data transformations and pipelines, which allows for data analytics to be performed directly on in-place data. Leveraging highly parallelized compute and storage enables data to move seamlessly across the environment using high-speed in-memory interconnects.
Separation of compute from data. This provides the ability to reduce data movement and duplication by substituting real-time computation and high-performance data infrastructure. It reduces complexity of data pipelines and increases data agility.
Shared data platform. This supports analytics, ML and deep-learning analytical activities on a single open data architecture. Different methods can be employed across the platform, such as advanced data analytics, data science tooling, modern data pipelines and data management frameworks — all within a single shared data environment.
As demand for these new capabilities increases, we expect to see converged analytics architecture becoming the default approach for large financial services organisations as there are significant benefits in adopting a common data platform.
EITN: How to better detect fraud and provide a safe and secure usage of financial products and services?
Joe: Financial institutions whose commercial and investment banking operations are increasingly being targeted know that the only way to fight fraud effectively is through the use of advanced technology. These institutions are fighting sophisticated criminals and need to have an equally sophisticated response. Financial institutions must evolve with digital transformation in order to detect and prevent cybercrime.
Maximising the use of technology, trusted data and the expertize of people will help fight financial crime. By relying on advanced data analytics, AI and ML capabilities, fraud and compliance units can spend their time working on more-complex fraud issues.
The answer lies in relying on advanced analytics and enterprise-wide data storage capabilities that support the use of artificial intelligence (AI) and machine learning (ML) approaches to stay one step ahead of criminals. AI is best suited to defend against today’s fast-changing and complex bank fraud, where new threats are under development every day. However, AI requires the ability to process vast amounts of data in both structured and unstructured forms.
According to Refinitv 2019 Financial Crime Report, the top key technology to prevent financial crime is cloud based data and technology, followed by AI and ML tools, and API technologies.
Maximising the use of technology, trusted data and the expertise of people will help fight financial crime. By relying on advanced data analytics, AI and ML capabilities, fraud and compliance units can spend their time working on more-complex fraud issues.
Manual investigation can be reduced through the use of complex algorithms powered by ML, often in conjunction with rules, a combination that offers significant advantages over purely rules-based fraud detection.
