Tag: Financial Industry

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What you need to know: The Future of FinTech, RegTech and Wealth Management in the Digital Space

The tipping point is here. High tech business intelligence tools with built-in machine learning algorithms and big data inputs were once reserved for the Fortune 500. Now, the FinTech fad has shifted from early stage adopter to mainstream money manager and former technophobes are starting to digitize their businesses from end to end. New low cost, user-friendly self-service tools that can produce rapid-fire insights and on-demand customer service are finally within reach and can provide family wealth managers the brain power they need without the additional headache. Synaptik, True Interaction’s “Plug, Play, Predict” machine platform is already serving companies in the space, providing value more quickly than industry norms.

Reuters white paper on the digitization of wealth management identified three drivers behind the mainstream movement towards FinTech:

– New tools for investment research, risk management, trade processing, compliance, and reporting
– New business models offering better, faster, cheaper variants of existing services in investment management and brokerage
– New marketplaces, new managers, and new financial products that are changing the way capital and risk are allocated

In this blog post, we’ll explore disruptive technologies traditional firms with limited IT expertise can use to beat the market, improve existing services and stay on top of an increasingly complex regulatory environment. By leveraging cloud, open source, big data, Artificial Intelligence, API and Chatbots, companies can create robust digital ecosystems that will win younger clients and increase profits across the board. Companies that continue to resist digital transformation run the risk of becoming less competitive while those that embrace the opportunity will benefit from supplementing talented human capital with technological know-how.

Courtesy of PWC

Beat the Market

Big name hedge funds and investment firms deploy AI to comb through the internet for new investment opportunities. The elusive “super-algo” can swallow huge amounts of information from news reports, databanks and social media platforms and quickly optimize portfolios to profit from microscopic ripples and seismic shifts in the market. While private family wealth managers have relied on traditional methods and experience to pinpoint good investment opportunities, machine learning can provide the edge they need to compete in a volatile world. Now, building data ecosystems that provide real-time information and time series data on company performance and consumer trends no longer requires a Ph.D. in data analytics or computer science.

When considering investment management software, companies should look for some key features including scenario simulation, modeling, portfolio rebalancing, performance metrics, yield curve analysis and risk analytics. Your software should also be flexible, adaptable and able to ingest structured and unstructured data. The costs of professional investment programs range from $1300 to $8000 but as the market matures costs are likely to go down.

Money Management on Demand

Wealth management firms have relied on traditional relationship-driven business models for decades. But the personal touch that keeps more senior clients happy may repel the next generation. To attract younger clientele, companies need to invest in on-demand, low-touch digital customer service models that provide better transparency and more autonomy to their clients. Creating a flexible digital strategy that allows different client segments to engage with their portfolio independently and with their advisor as little or as often as they want is key to success. EY’s report “Advice goes virtual” looks at the range of innovative wealth management models that are now available and highlights firms that have struck the perfect balance between automation and human capital. Companies like Personal Capital, Future Advisor and LearnVest provide digital platforms with phone-based financial advisor services to meet the needs of busy millennials and satisfy the clients that prefer a dedicated human that knows the future they want to make for themselves. EY’s chart on innovations in wealth management sums up the range of digital opportunities that clients are gravitating towards.


Courtesy of EY

Automated Compliance

Since the financial crisis, the cost of compliance has risen steeply. Tech Crunch reports that “the global cost of compliance is an estimated $100 billion per year. For many financial firms, compliance is 20% of their operational budget.” Innovations in RegTech, an offspring of FinTech, can automate certain components of the compliance process and have the potential to dramatically reduce the cost of doing business. The Institute for International Finance (IIF) defines “RegTech” as “the use of new technologies to solve regulatory and compliance requirements more effectively and efficiently.”

Since 2008, the increasing speed of regulatory change has kept wealth management firms in a state of paralysis. Companies are constantly playing catch up and readjusting procedures to meet new requirements. In the not so distant future, integrated RegTech solutions will connect directly with regulatory systems and automatically update formulae, allowing wealth management firms to refocus their resources on revenue generating activities.

Instead of producing lengthy paper reports for regulators, new RegTech solutions can generate and communicate required reports automatically. Instead of scouring hundreds of documents and spreadsheets on a quarterly basis, RegTech solutions will alert compliance managers to risks in real-time so they can be eliminated immediately. The possibilities are endless and the cumbersome and costly task of navigating the increasingly complex regulatory environment will continue to generate more innovations in this field. While RegTech is still in its infancy, small family wealth management firms should start investigating this growing subsector and use this disruptive technology to their advantage.

Traditional wealth management firms that continue to resist the digital revolution will begin to look antiquated, even to their most senior clientele. True Interaction specializes in building and executing digital transformation strategies for companies that don’t have IT expertise. Synaptik, True Interaction’s CMS for data, is already providing firms in the FinTech, RegTech and AdTech spaces with easy-to-use data management, visualization, and deep learning insights. Our experts are providing free consultations to help them assess their needs and start planning their digital future. Schedule your custom consultation here.

By Nina Robbins

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Blockchain 101 Self-Assessment

Blockchain is the new black. We’ve heard the term in conference calls, seen it on the cover of magazines and know it’s a hot topic on CNBC but the barrage of information makes it difficult to distinguish hype from reality. It’s clear that Blockchain will revolutionize the world but understanding how is mission critical. In this blog post we’ll cover the Blockchain essentials and the most frequently asked questions we’ve come across.

At The Art of Service we’ve developed a Blockchain self-assessment tool that professionals use to test the depth of their knowledge on the Blockchain concept and its potential. The Blockchain self-assessment covers numerous criteria related to a successful project – a quick primer version is available for you to download at the end of the article.

BLOCKCHAIN FREQUENTLY ASKED QUESTIONS:

What Is the Blockchain?

The problem with nearly all Blockchain explanations is that they supply too much detail upfront and use lingo that winds up leaving folks more confused than when they started. We are in the nascent stages of this technological revolution and it’s hard to predict how Blockchain will impact our institutions and our lives. Brand new Blockchain-related technologies are being built every day and the framework is evolving.

Here are some key definitions and ideas to help you understand the fundamental pillars behind this insurgent technology:

1. Blockchain is a technology that essentially disperses an account ledger. For those of you in the monetary management world, you know an account ledger as the trusted source of transactions or facts. The same is true with Blockchain but in lieu of existing in a great buckskin bound book or in a financial management program, Blockchains are run by a dispersed set of information handling resources working together to maintain that account ledger.
2. The Blockchain procedure of securely and permanently time-stamping and recording all transactions makes it very hard for a user to change the account book once a block in a Blockchain has been added.
3. Private Blockchains allow for distributing identical copies of an account book but only to a restricted amount of trusted contributors. This set of techniques, practices, procedures and rules is better suited for applications needing simplicity, speed, and greater clarity.
4. Users of the Distributed Account Ledger Technology (DLT) notably benefit from the efficiencies by generating a more robust ecosystem for real-time and secure data sharing.
5. Blockchain is only one of the various kinds of data constructions that provide secure and valid achievement of distributed agreement. The Bitcoin Blockchain, which uses Proof-of-Work mining, is the most common approach being used today. However, additional forms of DLT consensus exist such as Ethereum, Ripple, Hyperledger, MultiChain and Eris.

Blockchain: Who controls the risk?

Each party on a Blockchain has access to the entire database and its complete past. No single party controls the data or the information. Every party can substantiate the records of its transaction associates directly, without a mediator.

For public businesses, the conditions of Blockchain are very different. The identity of contributors must be known while permissioned Blockchains require no evidence of work. Over the next few years, Blockchain growing pains will hit the industry and support systems will begin to take shape. Today, Blockchain needs supporting infrastructure available for cloud or traditional database setups – there are no systems management tools, reporting tools or legacy configuration integrations in place.

Could Blockchain be the structural change the market needs?

Blockchain’s foundational technology is the biggest innovation computer science has seen in a long time. The thought of a dispersed database where trust is established through mass collaboration and clever code rather than a powerful institution is game-changing. Now it will be up to the larger business community to determine whether it will become the building block for the digitized economy or if it will be disregarded and perish. Now, building formidable and trustworthy Blockchain standards is the next step to turn this global opportunity into a reality.

Blockchain: What does the future hold?

There are many Blockchain and distributed account ledger setups emerging in the market including: BigchainDB, Billon, Chain, Corda, Credits, Elements, Monax, Fabric, Ethereum, HydraChain, Hyperledger, Multichain, Openchain, Quorum, Sawtooth, Stellar. The Block chain use cases span a number of industries including insurance, healthcare and finance but we are only scratching the surface of what’s possible.

Next, get started with the Blockchain Self-Assessment:

The Blockchain Self-Assessment Excel Dashboard provides a way to gauge performance against planned project activities and achieve optimal results. It does this by ensuring that Blockchain criteria are automatically prioritized and assigned; uncovering where progress can be made now; and what to plan for in the future.

To help professionals architect and implement best Blockchain practices for your organization, Gerard Blokdijk, author of The Art of Service’s Self Assessments provides a quick primer of the 49 Blockchain criteria for any business in any country.

Get the Blockchain Quick Exploratory Self-Assessment eBook here:

https://189d03-theartofservice-self-assessment-temporary-access-link.s3.amazonaws.com/Blockchain_Quick_Exploratory_Self-Assessment_Guide.pdf

About the Author

Gerard Blokdijk is the CEO of The Art of Service. He has been providing information technology insights, talks, tools and products to organizations in a wide range of industries for over 25 years. Gerard is a widely recognized and respected information specialist. Gerard founded The Art of Service consulting business in 2000. Gerard has authored numerous published books to date.

By Gerard Blokdijk

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Big Data – The Hot Commodity on Wall Street

Imagine – The fluorescent stock ticker tape speeding through your company stats – a 20% increase in likes, 15% decrease in retail foot traffic and 600 retweets. In the new economy, net worth alone doesn’t determine the value of an individual or a business. Social sentiment, central bank communications, retail sentiment, technical factors, foot traffic and event based signals contribute to the atmospheric influence encasing you company’s revenue.

NASDAQ recently announced the launch of the “NASDAQ Analytics Hub” – a new platform that provides the buy side with investment signals that are derived from structured and unstructured data, and unique to Nasdaq. Big Data is the new oil and Wall Street is starting to transform our crude data material into a very valuable commodity.

What does this mean for the future of business intelligence?

It means that businesses that have been holding on to traditional analytics as the backbone of boardroom decisions must evolve. Nasdaq has pushed big data BI tech squarely into the mainstream. Now, it’s survival of the bittest.

An early majority of businesses have already jumped onto the Big Data bandwagon, but transformation hasn’t been easy. According to Thoughtworks, businesses are suffering from “transformation fatigue – the sinking feeling that the new change program presented by management will result in as little change as the one that failed in the previous fiscal year.” Many companies are in a vicious cycle of adopting a sexy new data analytics tool, investing an exorbitant amount of time in data prep, forcing employees to endure a cumbersome onboarding process, getting overwhelmed by the complexity of the tool, and finally, giving up and reverting to spreadsheets.


“There is a gap and struggle with business operations between spreadsheets, enterprise applications and traditional BI tools that leave people exhausted and overwhelmed, never mind the opportunities with incorporating alternative data to enhance your business intelligence processes.” – Joe Sticca COO TrueInteraction.com – Synaptik.co

Now, the challenge for data management platforms is to democratize data science and provide self-service capabilities to the masses. Luckily, data management platforms are hitting the mark. In April, Harvard Business Review published results of an ongoing survey of Fortune 1000 companies about their data investments since 2012, “and for the first time a near majority – 48.4% – report that their firms are achieving measurable results for their big data investments, with 80.7% of executives characterizing their big data investments as successful.”

As alternative data like foot traffic and social sentiment become entrenched in the valuation process, companies will have to keep pace with NASDAQ and other industry titans on insights, trends and forecasting. Synaptik is helping lead the charge on self-service data analytics. Management will no longer depend on IT teams to translate data into knowledge.

“Now, with the progression of cloud computing and easy to use data management interfaces with tools like Synaptik, your able to bring enterprise control of your data analytics processes and scale into new data science revenue opportunities.” – Joe Sticca COO TrueInteraction.com – Synaptik.co

Synaptik’s fully-managed infrastructure of tools makes big-data in the cloud is fast, auto-scalable, secure and on-demand when you need it. With auto-ingestion data-transfer agents, and web-based interfaces similar to spreadsheets you can parse and calculate new metadata to increase dimensionality and insights, using server-side computing, which is a challenge for user-side spreadsheet tools.

By Nina Robbins

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Evolution of Big Data Technologies in the Financial Services Industry

Our previous post provides an industry analysis that examines the maturity of banking and financial markets organizations. The significant deviations from the traditional business model within the financial services industry in the recent years emphasize the increasing need for a difference in how institutions approach big data. The long-standing industry, so firmly entrenched in its decades-long practices, is seemingly dipping its toes into the proverbial pool of big data as organization recognize that its implementation is integral to a firm’s survival, and ultimately its growth. IBM’s Big Data @ Work survey reports that 26 percent of banking and financial markets companies are focused on understanding the concepts surrounding big data. On the other end of the spectrum, 27 percent are launching big data pilots, but the majority of the companies surveyed in this global study (47 percent) remains in the planning stage of defining a road map towards the efficient implementation of big data. For those organizations still in the stage of planning and refinement, it is crucial to understand and integrate these observed trends within financial technologies that can bolster a company’s big data strategy.

Customer Intelligence

While banks have historically maintained the monopoly on their customer’s financial transactions, the current state of the industry, with competitors flooding the market on different platforms, prevents this practice to continue. Banks are being transformed from product-centric to customer-centric organizations. Of the survey respondents with big data efforts in place, 55 percent report customer-centric objectives as one of their organization’s top priorities, if not their utmost aim. In order to engage in more customer-centric activities, financial service companies need to enhance their ability in anticipating changing market conditions and customer preferences. This will in turn inform the development and tailoring of their products and services towards the consumer, swiftly seizing market opportunities as well as improving customer service and loyalty.

Machine Learning

Financial market firms are increasingly becoming more aware of the many potential applications for machine learning and deep learning, two of the most prominent uses being within the fraud and risk sectors of this industry. The sheer volume of consumer information collected from the innumerable amount of transactions conducted through a plethora of different platforms daily calls for stronger protocols around fraud and risk management. Many financial services companies are just beginning to realize the advantageous inclusion of machine learning within an organization’s big data strategy. One such company is Paypal, which, through a combination of linear, neural network, and deep learning techniques, is able to optimize its risk management engines in order to identify the level of risk associated with a customer in mere milliseconds. The potential foreshadowed by these current applications is seemingly endless, optimistically suggesting the feasibility of machine learning algorithms replacing statistical risk management models and becoming an industry standard. The overall value that financial institutions can glean from the implementation of machine learning techniques is access to actionable intelligence based on the previously obscured insights uncovered by means of such techniques. The integration of machine learning tactics will be a welcome catalyst in the acceleration towards more real-time analysis and alerting.

IoT

When attempting to chart the future of financial technology, many point to the Internet of Things (IoT) as the next logical step. Often succinctly described as machine-to-machine communication, the IoT is hardly a novel concept, with the continual exchange of data already occurring between “smart” devices despite the lack of human interference. As some industries, such as in retail and manufacturing, already utilize this technology to some extent, it is not a far-fetched notion to posit that the financial service industry will soon follow suit. While there are those who adamantly reject the idea due to the industry being in the business of providing services as opposed to things, this would be a dangerously myopic view in this day and age. Anything from ATMs to information kiosks could be equipped with sensing technology to monitor and take action on the consumer’s’ behalf. Information collected from real-time, multi-channel activities can aid in informing how banks provide the best, most timely offers and advice to their customers.

For more information to empower your data science initiatives please visit us at www.Synaptik.co. We pride ourselves to empower every day users to do great data discovery without the need for deep core technical development skills.

Joe Sticca, Chief Operating Officer of True Interaction, contributed to this post.

By Justin Barbaro

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Technological Disruptions in the Financial Services Industry

The financial services industry has long boasted a resilient and steadfast business model and has proven to be one of the most resistant sectors when it comes to disruption by technology. In the recent years, however, a palpable change is overturning the ways and processes that have upheld this institution for so long. Organizations within this historically traditional and unyielding sector are realizing the need to not only assimilate into the digital era, but to embrace and incorporate it fully or else be overtaken but others who have opted to innovate rather than succumb under the pressure of this increasingly competitive industry.

Changing Dynamics

The relationship between banks and their customers have drastically changed from the time during which the traditional banking model was formulated. Perhaps more than any other commercial enterprise, banks retained control over the relationships they had with their customers. For the most part, the bank with which an individual was aligned often determined his or her financial identity as nearly all transactions were administered through one’s bank. Moreover, McKinsey reports that, historically, consumers very rarely flitted between different service providers because of the promising image of stability that the industry has worked hard to maintain. While this may have been the case in the past, the relational dynamics between banks and their customers are not the same today as they were nearly a decade or so ago.

Instead of being reliant to a single bank for all financial dealings, consumers have more options at their disposal, which they are taking full advantage of by engaging in transient relationships with multiple banks such as “a current account at one that charges no fees, a savings accounts with a bank that offers high interest, a mortgage with a one offering the best rate, and a brokerage account at a discount brokerage.” Competition between financial institutions is undeniably fiercer than ever, and it turns out that consumers are also being courted by new peer-to-peer services, such as PayPal, that allow those who opt to use these services to conduct financial transactions beyond the traditional banking means and organizations.

Data Growth

The sheer rise in players and competitors within this industry alone is enough to indicate another glaring issue: the sudden growth in volume of financial transactions. More transactions leads to an explosion of data growth for financial service providers, a predicament for which not many organizations are adequately prepared to handle. A study conducted by the Capgemini/RBS Global Payments estimates that the global volume for electronic payments is about 260 billion and growing between 15 and 22% for developing countries. The expansion of data points stored for each transaction, committed on the plethora of devices that are available to the consumer, is causing difficulties in the active defense against fraud and detection of potential security breaches. Oracle observes a shift in the way fraud analysis is being conducted, with it previously being performed over a small sample of transactions but which now necessitates the analysis of entire transaction history data sets.

Timely Insight

Shrinking revenues is one of the most prominent challenges for financial institutions to date, calling for a need to improve operational cost efficiencies. New financial technologies are being developed to address issues like this by leveraging the amount of available data that these financial institutions have access to and monetize it. Traditional Business Intelligence tools have been a staple in the industry for years, but it is usually limited in its capacity. A lot of the available BI tools work well in conjunction with business analysts when they are looking to find answers and solutions to specific conundrum. The key to revamping traditional banking frameworks in order to make it more competitive and agile in the current environment is to build and incorporate processes that are capable of revealing patterns, trends, and correlations in the data. Oracle posits that disruptive technologies in the financial sectors need to be able to do more than report, but also uncover. The technological ramifications of an evolving financial industry with a continuously expanding amount of data and the demand for real-time, data-driven decisions include the ability to detect unanticipated questions and simultaneously provide tangible solutions.

Denisse Perez, Content Marketing Analyst for True Interaction, contributed to this post.

By Joe Sticca

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Wrangling Data for Compliance, Risk, and Regulatory Requirements

N.B. This article addresses the financial services industry, however, the insight and tips therein are applicable to nearly any industry today. ~EIC)

The financial services industry has always been characterized by its long list of compliance, risk, and regulatory requirements. Since the 2008 financial crisis, the industry is more regulated than ever, and as organizations undergo digital transformation and financial services customers continue to do their banking online, the myriad of compliance, risk, and regulatory requirements for financial institutions will only increase from here. In a related note, organizations are continuing to invest in their infrastructure to meet these requirements. IDC Financial Insights forecasts that the worldwide risk information technologies and services market will grow from $79 billion in 2015 to $96.3 billion in 2018.

All of this means reams of data. Financial firms by nature produce enormous amounts of data, and due to compliance requirements, must be able to store and maintain more data than ever before. McKinsey Global Institute reported in 2011 that the financial services industry has more digitally stored data than any other industry.

To succeed in todays financial industry, organizations need to take a cumulative, 3-part approach to their data:

1. Become masters at data management practices.

This appears obvious, but the vast amount of compliance, risk, and regulatory requirements necessitate that organizations become adept at data management. Capgemini identified 6 aspects to data management best practices:

Data Quality. Data should be kept optimal through periodic data review, and all standard dimensions of data quality– completeness, conformity, consistency, accuracy, duplication, and integrity must be demonstrated.

Data Structure. Financial services firms must decide whether their data structure should be layered or warehoused. Most prefer to warehouse data.

Data Governance. It is of upmost importance that financial firms implement a data governance system that includes a data governance officer that can own the data and monitor data sources and usage.

Data Lineage. To manage and secure data appropriately as it moves through the corporate network, it needs to be tracked to determine where it is and how it flows.

Data Integrity. Data must be maintained to assure accuracy and consistency over the entire lifecycle, and rules and procedures should be imposed within a database at the design stage.

Analytical Modeling. An analytical model is required to parcel out and derive relevant information for compliance.

2. Leverage risk, regulatory, and compliance data for business purposes.

There is a bright side to data overload; many organizations aren’t yet taking full advantage of the data they generate and collect. According to PWC, leading financial institutions are now beginning to explore the strategic possibilities of the risk, regulatory, and compliance data they own, as well as how to use insights from this data and analyses of it in order to reduce costs, improve operational efficiency, and drive revenue.

It’s understandable that in today’s business process of many financial institutions, the risk, regulatory, and compliance side of the organization do not actively collaborate with the sales and marketing teams. The tendency toward siloed structure and behavior in business make it difficult to reuse data across the organization. Certainly an organization can’t completely change overnight, but consider these tips below to help establish incremental change within your organization:

Cost Reduction: Eliminate the need for business units to collect data that the risk, regulatory, and compliance functions have already gathered, and reduce duplication of data between risk regulatory, compliance, and customer intelligence systems. Avoid wasted marketing expenses by carefully targeting marketing campaigns based upon an improved understanding of customer needs and preferences.

Increased Operational Efficiency: Centralize management of customer data across the organization. Establish a single source of truth to improve data accuracy. Eliminate duplicate activities in the middle and back office, and free resources to work on other revenue generating and value-add activities.

Drive Revenue: Customize products based upon enhanced knowledge of each customer’s risk profile and risk appetite. Identify new customer segments and potential new products through better understanding of customer patterns, preferences, and behaviors. Enable a more complete view of the customer to pursue cross-sell and up-sell oppportunities.

3. Implement a thorough analytics solution that provides actionable insight from your data.

Today, it’s possible for financial organizations to implement an integrated Machine Learning component that runs in the background, that can ingest data of all types from any number of people, places, and platforms, intelligently normalize and restructure it so it is useful, run a dynamic series of actions based upon data type and whatever specified situations contexts your business process is in, and create dynamic BI data visualizations out-of-the-box.

Machine Learning Platforms like SYNAPTIK enable organizations to create wide and deep reporting, analytics and machine learning agents without being tied to expensive specific proprietary frameworks and templates, such as Tableau. SYNAPTIK allows for blending of internal and external data in order to produce new valuable insights. There’s no data modeling required to drop in 3rd party data sources, so it is even possible to create reporting and insight agents across data pools.

By Michael Davison