In the wake of the global COVID-19 pandemic, the financial services industry is in the throes of a digital renaissance. Banks and financial institutions invested heavily in adopting emerging technologies, such as Artificial Intelligence and Machine Learning, to improve “customer experience”, retain existing customers and attract millennial and GenZ customers.

Conversational banking has now become a prerequisite for all banks and financial institutions. It is not restricted to mere communication between financial institutions and their customers via text or chat but is much more than that. Today, conversational banking has come to mean a service that allows customers to interact with their banks easily, access banking services, avail timely assistance to fulfil their banking/financial needs and receive personalized financial advice.

The online communication channels that are commonly used include chats, text, voice messaging and AI-powered chatbots. Therefore, conversational banking is personalized, streamlined and seamless, real-time, relevant, and consistent. The AI-powered chatbots use not just Artificial Intelligence but also use Machine Learning and Natural Language Processing. This enables these chatbots to have smart and relevant “two-way conversations” with the banks’ customers, besides solving their basic banking problems.

Banks use chatbots and other messaging services to interact with customers, through their specific apps. However, no transactions are possible, as it is not interlinked to the banking system. It functions as a standalone function. It is possible to interact with the banks using chatbots, virtual assistants, and other interactive technologies in a format that is very close to natural language conversations. This is an improvement over the present customer interactions with the bank, including inperson and phone banking.

Moreover, with the millennials and GenZ being digitally savvy and wealth moving into their hands, banks and financial institutions need to invest in digital features that will attract this cohort. According to Cornerstone Advisors’ What’s Going on in Banking 2023, 25 percent of the 300 community-based financial institution executives said they were three-quarters or more done with their digital transformation efforts, while 29 percent said they had deployed chatbots. With more investment happening in the digital transformation sphere, chatbots will likely evolve to become customers’ “intelligent digital assistants”.

GPT-3 – the hottest buzzword

GPT or Generative Pre-trained Transformer is the hottest buzzword in the field of AI, specifically, in Natural Language Processing. GPT is a largescale Machine Learning model that uses deep learning techniques to support natural language conversations, suitable to a context and time. GPT-3 is the third iteration of the GPT language model, originally developed by OpenAI. GPT-3 uses past data and redictive analytics to enable conversational interactions.

It can mine internet data (small-sized text/pictures/videos) and generates large volumes of contextually relevant text/pictures/videos. It can perform various tasks, such as translating text from one language to another, writing songs/ poems, creating a new picture, creating new text/story, and even generating software code. This works on the sampling methodology and therefore, the larger the model or sampling size, the more accurate and sophisticated the conversation or interaction between the GPT-3 Chatbot and customer is likely to be.

When it was first released in 2018, GPT had 117 million parameters. Today, per the model sizes, GPT-3 has 175 billion parameters, which help the tool analyze relevant data. GPT-3 175B identifies patterns and associations, and studies the relevance of data contextually to help the conversational interface between itself and its customers. It works over different layers to develop a model. Each layer is an improvement over the previous one and the most recent layer will be the most intelligent at that point in time.

It is possible to edit in this model, i.e., remove any layer, insert/add another layer. This improves the ability of the model to serve at its optimum. Based on examples, GPT-3 can develop associations of various inputs and generate text-like sentences, passages, paragraphs, new songs, and pictures. It can also identify patterns and associated words/objects with each other. Thus, when asked to generate a sentence, passage, or song, based on the input training data, the output will seem as if it has been “created” by a human.

Click here to read more about ChatGPT and how it could help banks cut cost and provide more personalized customer experience.

Banks are moving into the cloud as part of their digitalization efforts, more purposefully and faster than ever. They are finally transforming from long-held monolithic systems and shedding apprehensions around regulatory compliance, security, and skills availability, and welcoming the transformation and advantages brought by the cloud.

In doing so, banks can use the latest cloud-native technologies, in conjunction with domain skills they have built over many years, to make headway in meeting stiff challenges posed by new-age fintech companies born in the cloud. They will have access to flexible and scalable IT infrastructure built on agile principles that allow them to meet evolving needs of modern-day banking operations and customers by quickly turning around innovative, personalized offerings.

The hassles of being overloaded during peak seasons will be a thing of the past as the cloud’s dynamic elastic scalability and rapid capacity provisioning help banks navigate through peak transaction loads, without impacting the quality or seamlessness of the customer experience. At the same time, operational costs of infrastructure are bound to go down with management and upkeep.

Public or private cloud?

Having decided to go with the cloud, the next question banks face is: Which deployment model to choose? What works better: single cloud or ploy cloud; public or private?

In a recent study of cloud adoption, we found that 41% of banks were on a private cloud, while 28% used a public cloud. Both models have their share of advantages and limitations. The public cloud model excels in situations where scale, flexibility, on-demand computing and elastic scaling are paramount. Latency, governance, data residency, and other areas of compliance can cause problems here.

On the other hand, the private cloud model is the better option when security, control, compliance, and customization override other requirements. However, they might face compatibility issues with certain legacy applications and capacity expansions.

Or a mix of both?

These are among the reasons why banks are now more amenable to a mix of the two models. Even the large and mid-sized financial institutions that previously only chose private cloud are now open to using public cloud to house their smaller, non-core applications. In the study mentioned earlier, 31% of respondents were using a hybrid approach: A winning combination of on-premise, private, and public cloud models that promises scalability, efficiency, and technology capabilities that stand the test of time.

Here, banks can choose to retain legacy systems, which are not cloud-ready, on their premises. In parallel, their technology decision-makers can work out the ideal mix of applications that can be distributed across private and public clouds depending on the use-case scenarios most relevant to their needs.

While they are making these decisions, it’s also important to evaluate the additional benefits of a multi-cloud arrangement. Banking institutions get to mitigate the risk of vendor lock-in and toggle between cloud service providers to seamlessly meet business and market requirements. They can select the most suitable cloud service provider for each workload and have more room for negotiating terms, as they have many vendors.

It will also prepare them for the future as regulations are expected to come into force, making it necessary for banks to use multiple cloud vendors. A 2021 Google Cloud Report reflects this shift in mindset: 88% of respondents were adopting a multi-cloud strategy.

The significance of hybrid, multi-cloud in digital transformation

This approach is the most viable for modernization and future-readiness. Still, banks are bound to encounter complexities that can impact interoperability and seamlessness within the cloud environment. There are inherent challenges such as application tier and data tier being distributed in different cloud environments. Other factors, such as heterogeneity of models and lack of standardized data replication tools between cloud service providers, will also lead to compromised outcomes.

Therefore, we recommend that banks go with cloud-native, cloud-agnostic solutions that can easily slide into a multi-cloud setup. This allows them to find the best-suited vendor and data configurations that enable seamless operations. In addition, they can opt for containerized deployments to ensure automated application development that contributes to overall efficiency.

Undisputedly, banks stand to gain plenty from migrating to the cloud with a hybrid, multi-cloud approach. However, rather than going all out on their cloud investment, they should tread this path of change wisely with careful planning to optimize the many advantages it enables. They can then aspire for greater benefits beyond cost efficiencies, business resilience, advanced analytics, and the ability to more quickly deploy, automate and innovate.

Thus armed, they can focus on offering optimal customer experiences while navigating through transaction loads on a massive scale.

This article was previously published in VentureBeat.

Lending, borrowing, selling, and buying have gone on for centuries. These are important aspects of finance, which are for the most part managed by centralized systems, long-established and governed by authorized banks and financial bodies. Traditionally, if a consumer wanted to take a car loan or mortgage on a new home or buy stocks or invest in funds or avail any kind of financial service, they would need to go through some form of middleman, namely the governing financial bodies like banks, exchanges etc.

In case of lending, banks and exchanges earn some percentage of the profit resulting from these transactions. Typically, to ensure security, they apply gatekeeping measures (KYC checks) for those requiring these financial services. As a result, the process becomes lengthy with many people involved and can create friction points in the formal credit system. Decentralized finance (DeFi), on the other hand, is aiming to cut out the middleman, and making lending and other financial transactions possible and more convenient between peers directly. At present, DeFi services can be enabled for a number of crucial areas of finance from lending to borrowing, funding, trading, derivatives, and insurance.

Decoding DeFi

The central idea behind DeFi is to facilitate the management of money through P2P transactions between individuals, merchants, and businesses without interventions by large financial institutions or corporations. It’s based on open-source technology without a controlling authority to deny access to any financial product/services sought by users. It also facilitates market exchanges round the clock.

DeFi is based on Blockchain technology, which enables financial applications and protocols with programmable functionality. Transactions on the blockchain are carried out automatically by smart contracts. Smart contracts include terms of agreement and the deal struck between concerned parties. These contracts set up a rules-based ecosystem where financial transactions such as lending, and investing can take place without needing third parties like banks and brokerage houses.

The transactions happen automatically when the conditions of the smart contract are met, as opposed to traditional finance where many people and systems can be involved in processing, verification, and logging of transactions. Transaction records are maintained on the immutable ledger and independently verified by thousands of computers around the globe.

From a consumer perspective, with just an internet connection, individuals can conduct financial transactions with peers. They have software to note every financial transaction and get it validated in distributed financial databases, which are accessible in various locations. These databases typically gather data from all users and rely on a consensus mechanism for verification. As a result, more of the work historically handled by banks and other financial institutions can now be performed between peers, directly. Here the concerned parties agree to provide cryptocurrency in exchange for goods/services without needing an intermediary or overseer.

If you would like to read more about decentralized finance and how it is changing the global lending landscape, click here.

Banks are moving into the cloud as part of their digitalization efforts, more purposefully and faster than ever. They are finally transforming out from long-held monolithic models systematically and shedding apprehensions around regulatory compliance, security, skills availability, and welcoming the transformation and advantages brought by the cloud.

In doing so, banks can use the latest cloud-native technologies, in conjunction with their domain skills that they have built over many years, to make headway in meeting the stiff challenge posed by the new-age fintech companies born in the cloud. They will have access to flexible and scalable IT infrastructure built on agile principles that allow them to meet evolving needs of modern-day banking operations and customers by quickly turning around innovative, personalized offerings. The hassles of being overloaded during peak seasons will be a thing of the past as the cloud’s dynamic elastic scalability and rapid capacity provisioning help banks navigate through peak transaction loads without impacting the quality or seamlessness of the customer experience. At the same time, operational costs of infrastructure are bound to go down with regards to its management and upkeep.

Having decided to go with the cloud, the next question banks face is: which deployment model to choose? What works better: single cloud or ploy cloud; public or private? In a recent study of cloud adoption, we found that 41% of the respondent banks were on a private cloud while 28% used a public cloud. Both models have their share of advantages and limitations. The public cloud model trumps in situations where scale, flexibility, on-demand computing, and elastic scaling are paramount. Latency, governance, data residency, and other areas of compliance can cause problems here. On the other hand, the private cloud model is the better option when security, control, compliance, and customization override other requirements. However, they might face compatibility issues with certain legacy applications and capacity expansion.

These are among the reasons why banks are now more amenable to a mix of the two models. More institutions are now prepared to move core functionalities into the cloud. Even the large and mid-sized financial institutions that earlier chose to only go with private cloud, are now open to using public cloud to house their smaller, non-core applications. The previously mentioned study also revealed that 31% of the respondents were using a hybrid approach – a winning combination of on-premise, private, and public cloud models that promises scalability, efficiency, and technology capabilities that stand the test of time.

In the case of the hybrid cloud model, banks can choose to retain legacy systems, which are not cloud-ready, on their premises. In parallel, their technology decision-makers can work out the ideal mix of applications that can be distributed across private and public clouds depending on the use-case scenarios most relevant to their needs.

While they are making these decisions, it’s also important to evaluate the additional benefits of a multi-cloud arrangement – banking institutions get to mitigate the risk of vendor lock-in, and toggle between cloud service providers to meet business and market requirements seamlessly. They can select the most suitable cloud service provider for each workload and have more room for negotiating terms as they have many empanelled vendors. It will also prepare them for the future as regulations are expected to come into force, making it necessary for banks to use multiple cloud vendors. A 2021 Google Cloud Report reflects this shift in mindset as it found 88% of respondents looking to adopt a multi-cloud strategy.

The significance of hybrid, multi-cloud in digital transformation

As banks take this major step towards modernization and future-readiness, it appears to be the most viable option at present. Even with this approach, however, banks are bound to encounter complexities that can impact interoperability and seamlessness within the cloud environment. There are inherent challenges such as application tier and data tier being distributed different cloud environments. Other factors such as heterogeneity of models and lack of standardized data replication tools between cloud service providers will also lead to compromised outcomes.

Therefore, we at Infosys Finacle recommend that banks go with cloud-native, cloud-agnostic solutions that can easily slide into a multi-cloud setup. This allows them to find the best-suited vendor and data configurations that enable seamless operations. In addition, they can opt for containerized deployments to ensure automated application development that contributes to overall efficiency.

Undisputedly, banks stand to gain plenty from migrating to the cloud with a hybrid, multi-cloud approach. However, rather than going all out on their cloud investment, banks should tread this path of change wisely with careful planning to optimize the many advantages it enables. They can then aspire for greater benefits beyond cost efficiencies, business resilience, advanced analytics, and being able to deploy, automate, and innovate faster. Thus armed, banks can focus better on offering optimal customer experiences while navigating through transaction loads on a massive scale.

This piece first appeared in VentureBeat in Dec 2022