Artificial Intelligence is the core of Fintech

As an expert in Fintech and big data analytics, I had to write this blog - which is essentially a transcript of my talk in Hewlett Packard to group of very talented & experience folks.

Big data is THE enabler of artificial intelligence. Financial industry generates a whole lot of data and this data forms the basis for powerful analytical tools that use Artificial Intelligence technologies which automates a whole lot of decision making.

What is AI?

At its highest level, Artificial Intelligence is an intelligent technology that leverages historical data and applies what is learned to current contexts to make predictions. AI combines various related terms: machine learning, natural language processing, deep learning, predictive analytics, etc.

Fintech & AI

In the current era of digitization and customer empowerment with mobile Internet, Banks and financial services companies are coming under intense pressure to compete with new age Fintech companies.

Fortunately, big banks and financial firms have huge amounts of customer data this can be leveraged along with newer tools to create & deliver exceptional and memorable experiences using technology.

After decades of research in AI technologies, AI is ready for prime time. According to a report by AI solutions market is estimated to reach $153 billion by 2020.

Today, AI is poised to become a key enabler of modern CRM solutions to Banks. Today many banks & firms use automated communication tools such as ChatBots to reach out to customers.

80% of executives believe artificial intelligence improves worker performance. 

Lets now take a look at these new tools and technologies.

AI:  One of the key technology trends upending the financial industry

Today's data-saturated world offers immense opportunities for financial institutions that know how to put this data to use by structuring it in the right way. As a result, an increasing number of financial institutions are adopting Artificial Intelligence (AI) to better serve their customers and increase their business growth. The explosive growth of structured and unstructured data, availability of new technologies such as cloud computing and machine learning algorithms, rising pressures brought by new competition, increased regulation and heightened consumer expectations have created a 'perfect storm' for the expanded use of artificial intelligence in financial services.

Artificial Intelligence with its advances in computing power, the ability to store and process Big Data, and instant access to advanced algorithms offers many opportunities for the financial sector. Harnessing Artificial Intelligence enables financial institutions to spot nonstandard behavior patterns when auditing financial transactions or to assess and analyze thousands of pages of tax changes. AI is destined to be the perfect tool to empower financial institutions' service efforts with genuinely intelligent tools to cut the time spent on handling lending requests, providing financial consultancies or opening bank accounts. Utilizing intelligent tools enable financial organizations to provide excellent customer experience across different channels. To remain relevant in a technology-driven world, financial pros will have to learn to combine their efforts with these intelligent tools.

Use cases of Artificial Intelligence in financial institutions

The financial sector is embracing Artificial Intelligence and machine learning to stay afloat and win over the digitally native customers. Harnessing the disruptive technology offers plenty of opportunities for financial institutions including: Customer support, transactions and helpdesk, data analysis and advanced analytics, underwriting loans and insurance, repetitive tasks and  performance, automated virtual assistants and Chatbots. Intelligent tools augment the capabilities of financial pros enabling them to easily identify customers' preferences and react with insight and emotional intelligence, which is essential for the development of meaningful customer relationships.

By leveraging intelligent tools, financial institutions and banks become technologically sophisticated and capable of meeting the financial needs of digitally savvy customers. Utilizing AI allows financial pros to analyze customers' buying patterns and red flag any irregularities and take preventive measures. Aienabled tools allow for making better risk decisions and conducting more accurate risk credit assessments.

Predictive scoring

Employing predictive scoring allows financial professionals to predict credit-related behavior, defaulting on loan payments, occurring an accident, client churn or attrition. Scoring backed by intelligence empower financial institutions to recognize creditors who will pay back a loan from those who will not pay based on the credit application's data. Financial pros can effectively apply scoring in forecasting of credit risk before granting a loan or when the loan is already granted.

Banks apply predictive scoring when forecasting the risk for a granted loan or when selecting optimal debt collection activities by assessing the credit related behavior.

Also See: 

1. Decision support with use of predictive models comparing to application of common sense rules or rules prepared by expert gives profit higher by 10-30%. 
2. 35% of executives say their decision relies mostly on internal data and analytics. 
3. By adopting an intelligent-computing program, some banks have experienced a 10% increase in sales of new products, a 20% savings in capital expenditures, a 20% increase in cash collections, and a 20% decline in churn. 

Computer intelligence with human touch

With the current virtual assistants (VC) and Chatbots revolution, along with the tremendous growth of messaging and social media apps, there is a great opportunity for organizations to optimize processes and deliver better customer experiences.

Chatbots are personal assistants that leverage messaging apps or outbound messaging and can run continual analysis of the information that is needed by the customer to ensure they get the relevant information through their preferred channel. Today's intelligent Chatbots and Virtual Customer Assistants can even take into consideration the context of the discussion and provide answers to several questions while analyzing the whole communication thread.

Natural language processing (NLP) is another intelligent tool for uncovering and analyzing the "messages" of unstructured text by using machine learning and Artificial Intelligence. It allows for providing context to language, just as human brains do. As a result, financial pros can gain a deeper understanding of customers' perception around their products, services and brand. NPL can be employed by customer service agents to more quickly route customers to the information they need.

Use Case: Nina, a customer service web-assistant developed by Swedbank, processes around 30,000 conversations focusing on 350 different queries each month. Nina had a first-contact resolution rate of 78% in the first three months of its operation.

Also See

1. The use of virtual customer assistants (VCAs) will jump by 1,000% by 2020. ()
2. The virtual digital assistant (VDA) market is estimated to reach $15.8 billion worldwide by 2021 with unique active consumer VDA users growing to 1.8 billion, and enterprise VDA users rising to 843 million.
3. Research firm MarketsandMarkets predicts the NLP market will reach $13.4 billion by 2020, a compound annual growth rate of 18.4 percent. 

Next best action for financial pros

Another benefit that AI offers for financial institutions is prescriptive analytics integrated with the next best action approach. Next best action is a customer-centric paradigm that considers various actions that can be taken for a specific contact and decides on the 'best' one. The next best action is determined by the customer's needs and interests on the one hand, and the business objectives and policies on the other.

Leveraging innovative technology can provide financial pros with recommendations about what steps they should take next to achieve a specified goal, such as the highest possible revenue or the highest level of engagement.

Utilizing the most innovative predictive decision-making technology can significantly improve the accuracy and effectiveness of financial activities. Through applying analytics, financial pros are able to better understand customer needs and drive higher customer value. Tech savvy financial pros are armed with the right tools to choose the most relevant and efficient process flow and ensure that an offered product or service meets the customer's needs.

Embracing intelligent tools allows financial institutions to focus on predicting customer behavior in order to drive value, managing multichannel interactions, and operating as an insight-driven business.

Utilizing predictive analytics to better understand your customers

Artificial Intelligence provides financial pros with the intelligent tool of predictive analytics that augments their capabilities to create exceptional and memorable customer experiences. Analyzing all internal and external customer data and converting it into actionable insights allows for not only providing real-time advice and solutions, but also anticipating future financial needs on a customer level. Banking analytics, combined with cognitive computing, provides financial pros with the ability to know each customer, provide customers with personalized offers, making one-to-one relationships a possibility. It accelerates financial institutions' ability to create individualized experiences for customers and realize tangible business benefits.

Applying predictive analytics enables financial institutions to:

• Deliver meaningful and emotionally satisfying digital experiences.
• Optimize product portfolio and enable numerous cross-sell and upsell opportunities to highly refined audiences.
• Convey personalized and relevant marketing messages to a specific audience at a specific stage of life.
• Create a complete customer view (360-degree) to personalize all customer touch points, while capturing digital data on a customer's preferences.

Also See:

1. 47% of organizations across industry now use predictive analytics to support business insight for risk purposes.
2. Artificial Intelligence Will Drive The Insights Revolution - Forrester
3. How is big data analytics transforming corporate decision-making?  

Closing Thoughts  

In the age of the customer and intelligent technology, financial institutions and banks are under increased pressure to pay attention to technological developments such as AI, and are quickly adapting to these changes. Intelligent tools along with Big Data offer financial institutions a huge opportunity to deliver exceptional and memorable experiences. Furthermore, Customer Relationship Management (CRM) solution backed with these sophisticated tools provide financial pros with the right blend of technology and human touch. An intelligent CRM solution ensures the complete view of your customers, keeping all their preferences and interests in the centralized repository accessible for financial pros, and enabling them to provide exceptional customer experience. With the AI-powered tools financial pros can exponentially enhance the customer journeys with more personalized approach. Embracing Artificial Intelligence is at the forefront of propelling the financial institutions through the digital age of the customer.

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Key Learnings:

1. Artificial Intelligence can become a success enabler of modern financial institutions

2. Computer intelligence use cases financial institutions can employ to become more
Customer centric

3. AI powered tools such as predictive scoring, advanced analytics, virtual assistants and Chatbots can help financial institutions and banks win over digitally native customers

Getting your Big Data Strategy right

An expert advice on what you need from big data analytics, and how to get there.

Business and technology leaders in most organizations understand the power of big data analytics—but few are able to harness that power in the way they want. The challenges are complex, and so are the technologies. Identifying and investing in key principles will help you navigate that complexity to find the right way to tap the growing pools of information available to your organization.

There are six main factors required to get a big data analytics platform right. Lets take a look at each one of them and explain how companies can get their big data right.

1. Blazing speed

Expectations around data are higher than ever. Business users and customers demand results almost instantly, but meeting those expectations can be challenging, especially with legacy systems. Speed is not the only factor in implementing a big data analytics strategy, but it's top of the list. Customers typically need to run queries on data sets that are 10 terabyte or larger and want results in few minutes.

Typical Business warehouse solutions would take 48 hours or more, In today's high speed business world, results after 48 hours is almost useless.

Time to insight is a top priority for any new analytics platform. Companies need to invest in High Performance Computing (HPC) to get the results in few minutes. With newer in-memory analytics systems  - such as SPARK or SAP HANA, the wait times can shrink to less than a second!

New solutions are fully optimized, enabling it to provide insights on time to fuel bottom-line results.

2. Scalable capacity

Today, It's a given that any big data analytics solution must accommodate huge quantities of data, but it also needs to grow organically with data volumes. Analytics solution must be able to grow in scale as the data size increases. Today, customers can't afford a "rip and replace" options when the database sizes grow.

Business needs a system that can handle all the data growth in a  way that is transparent to the data consumer or analyst - with very little downtime, if any at all. Capacity and computer expansion must all happen in the background.

3. Intelligent integration of legacy tools

Big Data Analytics must work with legacy tools so that business management can have seemless continuity. But it is also important to know which tools must be replaced and when.

Businesses have made investments in these older tools - such as Business Warehouse, Databases, ETL tools etc. Top management is comfortable with these legacy tools. But as data size grows newer data analysis tools will be needed and these new tools will have to work along with legacy tools.

4. Must play well with Hadoop

Big data and Hadoop has almost become synonymous with big data analytics. But Hadoop alone is not enough.While Hadoop is well known, it is built on generic low cost servers, it is also slow.

Hadoop, an open source big data framework, is a batch processing system, meaning that when a job is launched to analyze data, it goes into a queue, and it finishes when it finishes - i.e., users have to wait for results.

Today, Big data analysis needs to be fast - we are talking about high-concurrency in-memory analytics. Companies will still use Hadoop - but find newer ways to run Hadoop without incurring the performance penalties. Newer implementations of Hadoop (ver 2.7.x) and Spark will allow both systems to run in parallel.

5. Invest in data scientists

Organizations must build teams of data analytics experts. Not just hire data scientists, but also invest in tools that allow them to conduct more robust analyses on larger sets of data.

The key to move forward with best possible data analysis solution is to enable data scientists work with actual data sets and not a sample subset. The data analytics development environment must have the scale and size needed to work on actual data sizes, else the answers can go wrong and also leads to longer and more iterative development process.

6. Advanced analytics capabilities

Data Analytics tools and capabilities are rapidly evolving. Newer analytical tools use Artificial Intelligence (AI) tools as businesses move toward predictive analytics.

Big data has moved beyond reporting. Big data analytics is being used to answer very complex questions based on the data in your database. Analytics are now being more predictive, geospatial, and sentiment focused.

The shift toward predictive analysis and other advanced analysis has started. Organizations now—with the way data science has become more and more a corporate asset—there's definitely greater interest in becoming more predictive and more data-science savvy in nature.

Closing Thoughts  

Globally, data is growing at a very rapid rate: 40-50 percent per year. In this environment, every business is going to struggle against an overwhelming volume of data. New technologies are there that can help manage data at that speed and scale.

But having a right big data strategy is vital for success. As new tools, technologies emerge, it becomes critical to have the right strategy to incorporate them into the existing eco-system in a seemless non-disruptive way.

In this blog, I have highlighted 6 main aspects of a big data strategy that helps organizations to get its big data strategy right. 

Fintech Needs High-Performance Computing

Newer Fintech companies are planning to disrupt financial markets. According to Accenture, the newer Fintech companeis are targeting ever faster settlement times.

To compete, current incumbents will have to match the turnaround time of the newer Fintech companies. In order to get to such fast turnaround times with existing workloads - companies will need High Performance Computing (HPC)

Historically, Financial companies have been first adaptors of advanced computing technologies such as Mainframes in 1960's Unix servers in 1990's. Today, activities such as high-frequency trading, complex simulations and real-time analytics are built on dedicated data centers filled with a diverse set of HPC systems.

These HPC systems are used to gather, parse, analyze and act on huge amounts of data - often several Petabytes/day. Having greater computing power increases competitive advantages in the market.

Let us see how HPC aids in building competitive advantages.

The only way for financial companies to address challenges is to use HPC solutions. 

Now, lets look at what constitues  HPC systems. 

From a hardware perspective, HPC systems has four components:

Market Outlook

It is clear that HPC provides competitive advantages to financial companies. According to IDC, total global revenue for the HPC market (including servers, storage, software and services) will increase from $21 billion in 2014 to $31.3 billion by 2019!