Dru009

Part
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Part
01

List of Banking Analytics Products/Services

Companies offering banking analytics solutions that are used by banking leaders in the world include SAS, WNS, EXL, IBM, Axtria, Infosys Edgeverve, and MapR. As requested, I've populated the attached spreadsheet with details on each company. You'll also find a brief summary of my research below.

findings

SAS: Used by over 90% of the top global banks. SAS has a 30% market share for advanced and predictive analytics overall, non-industry specific.

WNS: Partner of over 20 of the world's leading banks and financial services companies.

EXL: EXL was identified as a leader in banking analytics by this report.

IBM: Client list includes First Tennessee Bank, Banco Bilbao Vizcaya Argentina, ABN AMRO, Northern Trust, and Tangerine.

Axtria: Axtria's client list includes major global banks and the company has $50 million in revenue.

EdgeVerve: Has a market share of 3.6% with clients such as IndusInd Bank, HPB, Standard Bank, RBL Bank, Mau Bank, Equity Bank, PMC Bank, and I&M Bank.

MapR: Has a market share of 3.16% with clients such as Eastern Bank, JP Morgan & Chase, Synchrony Financial, Mizuho Bank, and Credit Agricole.

conclusion

To wrap up, companies offering banking analytics solutions that are used by banking leaders in the world include SAS, WNS, EXL, IBM, Axtria, Infosys Edgeverve, and MapR.
Part
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Part
02

Top Trends in Banking Analytics

Global banking leaders have been shown to use data lakes, machine-learning techniques, Google-like search engines, modern data-exploration and data-visualization tools, tools to analyze text, voice, video, and images, capabilities to leverage real-time data, deep learning applications, and a switch in AI solution focus in their banking analytics.

In order to triangulate the top trends in banking analytics, we researched interviews from banking leaders or surveys of banks and financial institutions. After reviewing these sources, we were able to determine the top trends of global banking leaders based on the general trends outlined in the individual sources.

TOP TRENDS IN BANKING ANALYTICS

We have determined that the biggest analytical trends that global bank leaders are as follows:

1. Data lakes
Traditionally, companies in the banking industry pulled data every night and then manipulated such data into comprehensible structures that could be further analyzed. Although this type of analysis allowed companies to look back upon previous trends in their customers and services, "real time analytics [were] very limited" and thus it could be assumed that companies were not efficient in adjusting to changes in their market when real time analytics could have detected such changes. Instead, as suggested by Ryan Naudé, the "[spearhead of] the data solutions division at Entelect", companies switch to using data lakes. By switching, companies essentially remove the structuralization of the data, allowing users to "link up the data they need at the time they need it", allowing for the
scaling up and industrialization of the development and delivery of use cases.

2. Machine-learning techniques
As computers advance, so does the software that runs it, and the potential A.I. that goes along with it. Machine-learning techniques involve being able to recognize "insights and context" within analytical data, and then "establishing predictive patterns" using complex algorithms, or an "amalgamation of various machine learning algorithms". By being able to predict and analyze such patterns, machine-learning techniques can greatly reduce the time and resources needed to recognize changes in trends across the market, and adjust accordingly. To attest to this, McKinsey & Company Financial Services reported knowing a bank who, by using machine learning, was able to "cut [their "mainframe’s running time and the resulting costs"] by 15 percent".

3. Google-like search engines
Due to the rapid expansion of increasingly available information and data through search engines like Google, it is expected that data be readily available for access through fast search, as described by the "GAFA effect". Thus, it would make sense for a bank wanting to improve their analytics to have an advanced search engine, in order to provide quick and easy access to all of a bank’s data, including the source, definition, and all other information needed to use the data effectively.

4. Modern data-exploration and data-visualization tools
While initially creating algorithms needed to have better analytics is essential for the overall improvement in performance for a company, being able to understand such algorithms is just as important. Experts in the field may be able to understand the algorithms a company uses in order to analyze their data, but if a new analytical team hires into the company, they most likely will not understand the existing algorithm, and will "not trust what they regard as a black box".

5. Tools to analyze text, voice, video and images
As technology changes, so does the way that databases and algorithms have to change in order to analyze the new types of data that come with the technology. More traditional forms of data, like numbers, must now "be combined with new, unstructured pools of data, from social media or other external sources". Thus, the need for tools to analyze unstructured data like text, voice, video, and images, is increasing in order to continue improving analytics.

6. Capabilities to leverage real-time data
Going back upon how accessible information is with the rapid expansion of technology, it is important now more than ever to be able to access real-time data in order to analyze it. Being able to access and analyze real-time data gives companies a "competitive advantage" through "real-time decision [making]" based on "predictive analytics", enables "a comprehensive and robust approach to sales and service", and keeps the company relevant with consumers.

7. Deep learning applications
Deep learning is a type of machine learning that deals with the way data is represented. Thus, deep learning is crucial to the development of improved analytics in all fields, as it can quickly and reliably recognize patterns within data, therefore, "transforming data into actionable insights". Deep learning applications include, but are not limited to voice recognition and video analytics.

8. Switch in AI solution focus
As with many of the trends on this list already, AI and computer algorithms are becoming central to the world of analytics. Due to this, those who program and manage such AI have to ask themselves the most important question regarding the task in the creation of such AI: what should be the focus of this AI? In the past, the answer to this question has been to focus on the AI's effectiveness, which presumably provided companies with access to such AI a competitive advantage. However, "a new generation of AI solutions [is] focused on ... effectiveness more than on ... efficiency". The most efficient solution to a problem is not always the most effective solution. Take for example the creation of a new product by Company A. Company A could automatically send out an announcement on social media with a discount of the new product targeted towards the public. However, the more effective solution would have been to target the 20-27 year old age range in order to maximize sales of the said product. The same principle applies to banking.

SUMMARY

As stated above, data lakes, machine-learning techniques, Google-like search engines, modern data-exploration and data-visualization tools, tools to analyze text, voice, video, and images, capabilities to leverage real-time data, deep learning applications, and a switch in AI solution focus are the top trends global banking leaders use in their analytics. In general, many have to do with the development and advancement of technology, and as such, many trends are tending towards AI, or specific algorithms for structuring or searching through data.
Sources
Sources