=Paper=
{{Paper
|id=Vol-1905/recsys2017_poster13
|storemode=property
|title=Recommender Systems for Banking and Financial Services
|pdfUrl=https://ceur-ws.org/Vol-1905/recsys2017_poster13.pdf
|volume=Vol-1905
|authors=Andrea Gigli,Fabrizio Lillo,Daniele Regoli
|dblpUrl=https://dblp.org/rec/conf/recsys/GigliLR17
}}
==Recommender Systems for Banking and Financial Services==
https://ceur-ws.org/Vol-1905/recsys2017_poster13.pdf
Recommender Systems for Banking and Financial Services
Andrea Gigli Fabrizio Lillo Daniele Regoli
MPS Capital Services Università di Bologna Scuola Normale Superiore
Viale Mazzini, 23 Viale Quirico Filopanti 5 Piazza dei Cavalieri 7
Siena, Italy 53100 Bologna, Italy 40126 Pisa, Italy 56126
andrea.gigli@mpscs.it fabrizio.lillo@unibo.it daniele.regoli@sns.it
ABSTRACT data (client, branch and account identifiers) and traded item data
In this work we demonstrate the usefulness of the application of (type of asset, transaction currency, asset country, time to maturity,
Recommender Systems in the financial domain. Specifically we complexity, industrial sector, industrial group, industrial sub-group,
investigate a dataset, made available by a major European bank, rating, coupon type, trading channel, buy-sell type).
containing the purchases of a large set of investment assets by Having no information on the traded volume per transaction
200k investors. We also present some preliminary results of the nor the client total wealth at the time of each trade, we model the
application of network analysis via statistical validation to identify recommendation problem on the basis of the binary information
clusters of investment assets. purchased/not purchased item.
KEYWORDS Implicit feedback recommender for financial
Finance, Collaborative Filtering, Networks, Statistical Validation investments
ACM Reference format: To better capture clients’ preferences we compare three different
Andrea Gigli, Fabrizio Lillo, and Daniele Regoli. 2017. Recommender Systems RecSys algorithms. All of them required to test different combina-
for Banking and Financial Services. In Proceedings of RecSys 2017 Posters, tions of features at our disposal in order to define user and item
Como, Italy. Copyrights held by the authors. , August 27-31, 2 pages. entities. After some trials and analysis, we defined the user as a com-
bination of client ID and bank branch, and the item as a combination
Introduction of asset type, country, time to maturity, coupon type, industrial sec-
tor and rating.The results for other aggregations are qualitatively
Banking and Financial Services, being them provided by incumbent
analogous.
Banks or by FinTech companies, are looking seriously at machine
The first algorithm we tested is the Bayesian Personalized Rank-
learning and information retrieval fields in order to leverage the
ing algorithm [3] where we use a matrix factorization method
data at their disposal to provide tailored services and customized
maximizing the posterior probability of user preference structure,
experiences to their customers.
and tune model’s parameters via 5-fold cross-validation. The sec-
One of the fields of computer science which can support this
ond one is the Alternating Least Squares algorithm [1] using 30
attempt is the one represented by Recommender Systems (RecSys),
latent factors and a regularization factor equal to 0.01. The third
which has been heavily investigated in the last years by the research
one is an adaptation of the Word2Vec algorithm [2] that we call
community as well as the most promising companies in the e-
Asset-Embedding in the following. In this case we treated the clients’
commerce and entertainment fields.
portfolios as they were documents, each asset as a word, and vector-
In this work we show the usefulness of some RecSys algorithms
represented each asset by the portfolio it belongs to via continuous
in suggesting investment assets to a large panel of investors. This
bag of words in a 300 dimension space.
is done by using a large dataset provided by a major European bank
The RecSys algorithms mentioned above are evaluated through
and comparing the performance of three different RecSys against
various tests against two benchmark algorithms based on most pop-
two baseline models in the task of suggesting investment assets.
ular items by number of users (POP.u) or by number of transactions
(POP.trans):
The Dataset
(1) Average Accuracy of the user preference structure (see [3]);
The recommender system implementation and analysis have been
(2) Expected percentile ranking, as defined in [1] (the lower, the
done on a dataset with financial investment information, made
better);
available to us by a European bank during a research collaboration
(3) the Area Under the ROC curve.
program, which contains 224,885 clients, 1,288,315 transactions and
information related to 7 different asset types, 23 rating levels, 6 Other metrics (e.g. novelty and coverage) have been calculated but
order channels, 12 industrial sectors, 8 maturity buckets, 5 coupon are left out for lack of space. Different train/test sampling method-
types, 2 product complexity levels. ologies were used:
The records span a period of twelve months and all data entries (1) leave-one-out: removing randomly from train one purchased
are properly hashed, anonymized and organized as a table, where asset for each user (who has at least 5 purchases);
each record represents a purchase defined by: execution date, user (2) leave-last-out: removing from train the last (in time) asset
purchased by each user;
RecSys 2017 Poster Proceedings, August 27-31, Como, Italy.
Copyrights held by the authors. (3) 20% level sampling: removing randomly from train 20% of
interactions.
RecSys 2017 Poster Proceedings, August 27-31, Como, Italy.
Copyrights held by the authors. Andrea Gigli, Fabrizio Lillo, and Daniele Regoli
Table 1: Evaluation metrics for leave-last-out test method-
ology, with variable number of most purchased items ex-
cluded from test set.
most purchased recommender Average
Rank AUC
excluded items system Accuracy
BPR-MF 0.961 3.878 0.970
Asset Embedding 0.951 4.975 0.950
0 ALS 0.954 4.590 0.954
POP.u 0.949 5.119 0.958
POP.trans 0.950 5.044 0.958
BPR-MF 0.941 5.919 0.951
Asset Embedding 0.906 9.389 0.903
20 ALS 0.909 9.080 0.906
POP.u 0.916 8.404 0.926
POP.trans 0.917 8.286 0.927
BPR-MF 0.885 11.537 0.914
Asset Embedding 0.859 14.105 0.874
50 ALS 0.917 8.259 0.913 Figure 1: Communities (pink regions) of assets detected
POP.u 0.825 17.472 0.819 on the statistically filtered asset graph projection. Color de-
POP.trans 0.820 18.032 0.817
notes sector attribute.
Due to limited amount of space, we here report the results for the the number of purchased objects in the different communi-
leave-last-out case only. ties.
Given that a good RecSys should give suggestions relevant and
As an example of the possible network analysis, Figure 1 shows
specific to the user and expand user’s taste into neighboring areas,
the statistically filtered network derived by applying the validation
we run the above tests after removing n = {0, 20, 50} most popular
algorithm to the bipartite network with the same specification of
items from the test set. In this way if a RecSys performs well with
users and items as in Table 1, for 1% confidence threshold. There are
0 popular items removed and poorly with 50, it is reasonable to
4 big connected communities, 2 smaller ones (but still connected)
deduce that maybe it is just good in suggesting popular items but
and 6 small isolated communities. Color of nodes (i.e. of assets)
not items related to the specific interests of the user.
refers to different value of sector attribute. As an example, the
Table 1 displays the results of our study for the leave-last-out
light-blue sector, Governmental assets, results to be statistically
train/test sampling case.It shows that all the RecSys we propose
over-expressed in the rightmost community, and under-expressed
perform extremely well on the dataset at our disposal, in terms of
in the leftmost and in the bottom one. This evidence indicates that
both average accuracy and ranking structure (expected percentile
statistically filtered investors’ decisions could be used to cluster
ranking - Rank - and AUC). BPR-MF is the best performer when
assets: a promising starting point to build a statistically guided
no popular items is excluded from the test set and its advantage
algorithm for recommendations. This is part of a work in progress
doesn’t reduce when we increase the number of popular purchased
for future publication.
item removed from the test set. ALS performs similarly well, while
Asset Embedding performs better than POPs when the number of
ACKNOWLEDGMENTS
popular items excluded from the test set is at least 50, but it never
beats BPR-MF and ALS. The authors would like to thank MPS Bank for supporting the
collaboration, Francesco Mainieri (MPS) for essential support in
Toward a network-based RecSys for banking and extracting the data and Franco Maria Nardini (CNR, Pisa) for useful
comments. FL and DR acknowledge support by the European Com-
financial services
munity’s H2020 Program under the scheme INFRAIA-1- 2014-2015:
Besides the training of the recommender system shown above and Research Infrastructures, Grant Agreement No. 654024 SoBigData:
the detailed test previously mentioned, we performed an analysis Social Mining & Big Data Ecosystem.
of the dataset seen as it were a bipartite network users → items.
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